WO2021262408A1 - Analyse de discours améliorée - Google Patents

Analyse de discours améliorée Download PDF

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Publication number
WO2021262408A1
WO2021262408A1 PCT/US2021/035515 US2021035515W WO2021262408A1 WO 2021262408 A1 WO2021262408 A1 WO 2021262408A1 US 2021035515 W US2021035515 W US 2021035515W WO 2021262408 A1 WO2021262408 A1 WO 2021262408A1
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discourse
tree
text
communicative
sentence
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PCT/US2021/035515
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English (en)
Inventor
Boris Galitsky
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Oracle International Corporation
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Priority claimed from US17/003,593 external-priority patent/US11748572B2/en
Application filed by Oracle International Corporation filed Critical Oracle International Corporation
Priority to JP2022546461A priority Critical patent/JP2023531345A/ja
Priority to CN202180005817.6A priority patent/CN114902230A/zh
Publication of WO2021262408A1 publication Critical patent/WO2021262408A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G06F40/35—Discourse or dialogue representation

Definitions

  • This disclosure is generally concerned with linguistics. More specifically, this disclosure relates to generation of improved discourse trees.
  • Linguistics is the scientific study of language.
  • One aspect of linguistics is the application of computer science to human natural languages such as English. Due to the greatly increased speed of processors and capacity of memory, computer applications of linguistics are on the rise.
  • computer-enabled analysis of language discourse facilitates numerous applications such as automated agents that can answer questions from users. But such applications are unable to leverage rich discourse related information to answer questions, perform dialog management, or provide recommendations systems.
  • a method of improving an accuracy of a discourse tree includes: creating a discourse tree from text by identifying elementary discourse units in the text, wherein the discourse tree includes nodes, each nonterminal node of the nodes in the discourse tree representing a rhetorical relationship between two elementary discourse units and each terminal node of the nodes of the discourse tree associated with an elementary discourse unit; identifying, in the discourse tree, a rhetorical relation of type elaboration or joint, wherein the rhetorical relation relates a first elementary discourse unit and a second elementary discourse unit, and wherein the first elementary discourse unit and the second elementary discourse unit form a reference sentence; determining a syntactic generalization score for each candidate sentence of a set of candidate sentences, wherein each candidate sentence has a corresponding semantic relation, the determining including: identifying one or more common entities between the candidate sentence and the reference sentence; and computing a syntactic generalization score equal to a number of the identified one or more common entities; selecting the candidate sentence having a highest syntactic generalization score of
  • creating the discourse tree from the text includes: providing the text to a classification model; and identifying, using the classification model, the first elementary discourse unit, the second elementary discourse unit, and the rhetorical relation.
  • the updated rhetorical relation is one of purpose, means, cause, or temporal sequence.
  • each of the one or more common entities share a common part of speech between the candidate sentence and the reference sentence.
  • the method further includes forming a response from the updated discourse tree and outputting the response to an external device.
  • the method includes forming, from each candidate sentence, a first syntactic parse tree; and forming, from the reference sentence, a second syntactic parse tree, wherein identifying the one or more common entities between the candidate sentence and the reference sentence includes, for each common entity, identifying the common entity in the first syntactic parse tree and the second syntactic parse tree.
  • the method includes forming a communicative discourse tree from the updated discourse tree by matching each fragment that has a verb to a verb signature; identifying that the text includes argumentation by applying a classification model trained to detect argumentation to the communicative discourse tree; and forming a response from the text and outputting the response to an external device.
  • the method includes forming a communicative discourse tree from the updated discourse tree by matching each fragment that has a verb to a verb signature; identifying that the text includes an argument corresponding to a claim by applying a classification model trained to detect argumentation to the communicative discourse tree; evaluating a consistency of the argument with respect to itself and with respect to a domain definition clause that is associated with a domain of the text by solving a logic system that includes: a fixed part including a term of the claim and the domain definition clause, and a variable part including a set of defeasible rules from the communicative discourse tree and a fact from a communicative action of the communicative discourse tree; and responsive to determining that the evaluated consistency is greater than a threshold, forming a textual response from the text and outputting the textual response to an external device.
  • the above methods can be implemented as tangible computer-readable media and/or operating within a computer processor and attached memory.
  • FIG. 1 depicts an exemplary discourse tree environment in accordance with an aspect.
  • FIG. 2 depicts an example of a discourse tree in accordance with an aspect.
  • FIG. 3 depicts a further example of a discourse tree in accordance with an aspect.
  • FIG. 4 depicts illustrative schemas in accordance with an aspect.
  • FIG. 5 depicts a nodedink representation of the hierarchical binary tree in accordance with an aspect.
  • FIG. 6 depicts an exemplary indented text encoding of the representation in FIG. 5 in accordance with an aspect.
  • FIG. 7 depicts an exemplary discourse tree for an example request about property tax in accordance with an aspect.
  • FIG. 8 depicts an exemplary response for the question represented in FIG. 7.
  • FIG. 9 illustrates a discourse tree for an official answer in accordance with an aspect.
  • FIG. 10 illustrates a discourse tree for a raw answer in accordance with an aspect.
  • FIG. 11 illustrates a communicative discourse tree for a claim of a first agent in accordance with an aspect.
  • FIG. 12 illustrates a communicative discourse tree for a claim of a second agent in accordance with an aspect.
  • FIG. 13 illustrates a communicative discourse tree for a claim of a third agent in accordance with an aspect.
  • FIG. 14 illustrates parse thickets in accordance with an aspect.
  • FIG. 15 illustrates an exemplary process for building a communicative discourse tree in accordance with an aspect.
  • FIG. 16 illustrates a discourse tree and scenario graph in accordance with an aspect.
  • FIG. 17 illustrates forming a request-response pair in accordance with an aspect.
  • FIG. 18 illustrates a maximal common sub-communicative discourse tree in accordance with an aspect.
  • FIG. 19 illustrates a tree in a kernel learning format for a communicative discourse tree in accordance with an aspect.
  • FIG. 20 illustrates an exemplary process used to implement a rhetoric agreement classifier in accordance with an aspect.
  • FIG. 21 illustrates a chat hot commenting on a posting in accordance with an aspect.
  • FIG. 22 illustrates a chat hot commenting on a posting in accordance with an aspect.
  • FIG. 23 illustrates a discourse tree for algorithm text in accordance with an aspect.
  • FIG. 24 illustrates annotated sentences in accordance with an aspect.
  • FIG. 25 illustrates annotated sentences in accordance with an aspect.
  • FIG. 26 illustrates discourse acts of a dialogue in accordance with an aspect.
  • FIG. 27 illustrates discourse acts of a dialogue in accordance with an aspect.
  • FIG. 28 depicts an exemplary communicative discourse tree in accordance with an aspect.
  • FIG. 29 depicts an exemplary communicative discourse tree in accordance with an aspect.
  • FIG. 30 depicts an exemplary communicative discourse tree in accordance with an aspect.
  • FIG. 31 depicts an exemplary communicative discourse tree in accordance with an aspect.
  • FIG. 32 depicts an example communicative discourse tree in accordance with an aspect.
  • FIG. 33 depicts an example communicative discourse tree in accordance with an aspect.
  • FIG. 34 depicts an example communicative discourse tree in accordance with an aspect.
  • FIG. 35 depicts an example communicative discourse tree in accordance with an aspect.
  • FIG. 36 depicts an exemplary process for using machine learning to determine argumentation in accordance with an aspect.
  • FIG. 37 is a fragment of a discourse tree in accordance with an aspect.
  • FIG 38 depicts a discourse tree for a borderline review in accordance with an aspect.
  • FIG. 39 depicts a discourse tree for a sentence showing compositional semantic approach to sentiment analysis in accordance with an aspect.
  • FIG. 40 depicts an exemplary method for validating arguments in accordance with an aspect.
  • FIG. 41 depicts an exemplary communicative discourse tree for an argument in accordance with an aspect.
  • FIG. 42 depicts an exemplary method for validating arguments using defeasible logic programming in accordance with an aspect.
  • FIG. 43 depicts an exemplary dialectic tree in accordance with an aspect.
  • FIG. 44 depicts a discourse tree and a semantic tree, in accordance with an aspect.
  • FIG. 45 depicts a discourse tree and a semantic tree, in accordance with an aspect.
  • FIG. 46 is a flowchart of an exemplary process for generating improved discourse trees, in accordance with an aspect.
  • FIG. 47 depicts a generalization of a sentence and a template with known semantic relation, in accordance with an aspect
  • FIG. 48 depicts alignment between two sentences, in accordance with an aspect.
  • FIG. 49 depicts a simplified diagram of a distributed system for implementing one of the aspects.
  • FIG. 50 is a simplified block diagram of components of a system environment by which services provided by the components of an aspect system may be offered as cloud services in accordance with an aspect.
  • FIG. 51 illustrates an exemplary computer system, in which various aspects of the present invention may be implemented.
  • aspects disclosed herein provide technical improvements to the area of computer- implemented linguistics. More specifically, disclosed solutions generate improved discourse trees by determining an updated rhetorical relation for a discourse tree from a semantic relation of a semantic representation of text. Improved discourse trees can enable improved applications that use discourse trees such as dialogue management, reasoning, argumentation detection, search, and navigation. An improved discourse tree can be augmented with communicative actions, thereby forming a communicative discourse tree (“CDT”). A communicative action is a cooperative action undertaken by individuals based on mutual deliberation and argumentation. [0065] Therefore, technical advantages of some aspects therefore include discourse trees that more accurately represent a source text and improved autonomous agents such as autonomous agent that can validate argumentation in text using CDTs. For instance, a CDT can be used to determine agreement between sentences, or detect or validate argumentation in text. A valid argument is an argument that is logically consistent, for example, for which the text of the argument supports the premise of the argument.
  • a rhetoric classification application executing on a computing device receives a question from a user.
  • the rhetoric classification application generates a communicative discourse tree for the question.
  • a communicative discourse tree is a discourse tree that includes communicative actions.
  • the rhetoric classification application accesses a database of potential answers to the question.
  • the rhetoric agreement application determines a level of complementarity between the question and each potential answer. Responsive to determining that the level of complementarity is above a threshold, the rhetoric agreement classifier provides the answer to the user, for example, via a display device.
  • a rhetoric classification application generates a communicative discourse tree (CDT) from input text and uses machine learning to validate argumentation in a subset of the text.
  • the rhetoric classification application creates a logic program by extracting facts and defeasible rules from the communicative discourse tree and provides the facts and defeasible rules to a logic system such as Defeasible Logic Programming (DeLP).
  • the logic system accesses fixed rules and domain specific definition clauses and solves the logic program, thereby determining whether the argumentation is valid (e.g., the argument supports the claim), or invalid (e.g. the argument does not support the claim).
  • course tree or “DT” refers to a structure that represents the rhetorical relations for a sentence of part of a sentence.
  • a “rhetorical relation,” “rhetorical relationship,” or “coherence relation” or “discourse relation” refers to how two segments of discourse are logically connected to one another. Examples of rhetorical relations include elaboration, contrast, and attribution.
  • a “sentence fragment,” or “fragment” is a part of a sentence that can be divided from the rest of the sentence.
  • a fragment is an elementary discourse unit. For example, for the sentence “Dutch accident investigators say that evidence points to pro- Russian rebels as being responsible for shooting down the plane,” two fragments are “Dutch accident investigators say that evidence points to pro-Russian rebels” and “as being responsible for shooting down the plane.”
  • a fragment can, but need not, include a verb.
  • signature or “frame” refers to a property of a verb in a fragment.
  • Each signature can include one or more thematic roles. For example, for the fragment “Dutch accident investigators say that evidence points to pro-Russian rebels,” the verb is “say” and the signature of this particular use of the verb “say” could be “agent verb topic” where “investigators” is the agent and “evidence” is the topic.
  • thematic role refers to components of a signature used to describe a role of one or more words.
  • agent and “topic” are thematic roles.
  • nuclearity refers to which text segment, fragment, or span, is more central to a writer's purpose. The nucleus is the more central span, and the satellite is the less central one.
  • communicative verb is a verb that indicates communication.
  • the verb “deny” is a communicative verb.
  • communicative action describes an action performed by one or more agents and the subjects of the agents.
  • claim is an assertion of truth of something. For example, a claim could be “I am not responsible for paying rent this month” or “the rent is late.”
  • an “argument” is a reason or set of reasons set forth to support a claim.
  • An example argument for the above claim is “the necessary repairs were not completed.”
  • a “argument validity” or “validity” refers to whether an argument that supports a claim is internally and consistent. Internal consistency refers to whether the argument is consistent with itself, e.g., does not contain two contradictory statements. External consistency refers to whether an argument is consistent with known facts and rules.
  • a “logic system” or “logic program” is a set of instructions, rules, facts, and other information that can represent argumentation of a particular claim. Solving the logic system results in a determination of whether the argumentation is valid.
  • a “dialectic tree” is a tree that represents individual arguments.
  • a dialectic tree is solved to determine a truth or falsity of a claim supported by the individual arguments.
  • Evaluating a dialectic tree involves determining validity of the individual arguments.
  • FIG. 1 depicts an exemplary discourse tree environment in accordance with an aspect.
  • FIG. 1 depicts computing device 101, input text 130, and argumentation indicator 165.
  • Computing device 101 includes one or more of application 102, discourse parser 104, answer database 105, rhetoric agreement classifier 120, and training data 125.
  • Examples of computing devices include devices 4902, 4904, 4906, and 4908 and cloud computing device 5002 client devices 5004, 5006, 5008 depicted in FIGS. 49 and 50 respectively.
  • application 102 generates discourse trees having a higher quality and/or accuracy than with respect to previous solutions.
  • discourse parser 104 generates a discourse tree from input text 130.
  • Application 102 analyzes the discourse tree and generates a semantic representation such as an abstract meaning representation (AMR) graph.
  • AMR is a semantic representation language.
  • AMR graphs are rooted, labeled, directed, acyclic graphs (DAGs), including whole sentences. From the AMR graph, using the techniques disclosed herein, application 102 generates an improved discourse tree, which can in turn, be used to perform discourse analysis. An example of a process for creating an improved discourse tree is discussed with respect to FIG. 45.
  • Input text 130 can be a single question or a stream of questions.
  • Application 102 creates question communicative discourse tree from input text 130 and selects one or more candidate answers. The answers can be obtained from an existing database such as answer database 105.
  • Input text 130 can be generated by any mobile device such as a mobile phone, smart phone, tablet, laptop, smart watch, and the like.
  • a mobile device can communicate via a data network to computing device 101. In this manner, a mobile device can provide a question, e.g., from a user, to computing device 101.
  • application 102 determines the most suitable answer. Different methods can be used.
  • application 102 can create a candidate answer communicative discourse tree for each candidate answer and compare question communicative discourse tree with each candidate discourse tree.
  • Application 102 identifies a best match between question communicative discourse tree and the candidate answer communicative discourse trees.
  • the application 102 accesses or queries a database for the text from the best communicative discourse tree.
  • Application 102 then sends text associated with the second communicative discourse tree to a mobile device.
  • application 102 creates an answer communicative discourse tree for each candidate answer.
  • Application 102 then, for each candidate answer, creates a question-answer pair that includes the input text 130 and the candidate answer.
  • Application 102 provides the question-answer pairs to a predictive model such as rhetoric agreement classifier 120.
  • a predictive model such as rhetoric agreement classifier 120.
  • application 102 determines whether the question-answer pair is above a threshold level of matching, e.g., indicating whether the answer addresses the question. If not, the application 102 continues to analyze additional pairs that include the question and a different answer until a suitable answer is found.
  • communicative discourse trees the rhetorical agreement and communicative actions between the question and answer can be accurately modeled.
  • application 102 uses rhetoric agreement classifier 120 to determine whether argumentation is present or absent from input text 130.
  • rhetoric classification application 102 accesses input text 130, which reads: “[t]he rent was properly refused....The landlord contacted me, the tenant, and the rent was requested. However, I refused the rent since I demanded repair to be done. I reminded the landlord about necessary repairs, but the landlord issued the three-day notice confirming that the rent was overdue. Regretfully, the property still stayed unrepaired.”
  • Input text 130 thus includes a claim “the rent was properly refused” and an associated argument “The landlord contacted me, the tenant, and the rent was requested. However, I refused the rent since I demanded repair to be done. I reminded the landlord about necessary repairs, but the landlord issued the three-day notice confirming that the rent was overdue. Regretfully, the property still stayed unrepaired.”
  • application 102 determines a communicative discourse tree from input text 130 and provides the communicative discourse tree to a trained classifier such as rhetoric agreement classifier 120.
  • Application 102 receives a prediction of whether argumentation is present from rhetoric agreement classifier 120.
  • Application 102 provides the prediction as argumentation indicator 165.
  • Rhetoric agreement classifier 120 compares the communicative discourse tree with communicative discourse trees identified in a training set as positive (argumentation) or negative (no argumentation). An exemplary process is discussed with respect to FIG. 36.
  • application 102 can validate argumentation present in input text 130.
  • An exemplary process is discussed with respect to FIG. 40.
  • application 102 determines a presence of argumentation, for instance, by using rhetoric agreement classifier 120.
  • Application 102 can then determine whether a detected argument is valid or invalid.
  • Defeasible Logic Programming can be used.
  • An exemplary process is discussed with respect to FIG. 42.
  • Application 102 can output argumentation indicator 165 can indicate whether argumentation is detected, and if so, whether an argument is valid or invalid.
  • Linguistics is the scientific study of language.
  • linguistics can include the structure of a sentence (syntax), e.g., subject-verb-object, the meaning of a sentence (semantics), e.g. dog bites man vs. man bites dog, and what speakers do in conversation, i.e., discourse analysis or the analysis of language beyond the sentence.
  • RST Rhetoric Structure Theory
  • RST helped enabled the analysis of discourse. More specifically RST posits structural blocks on at least two levels, a first level such as nuclearity and rhetorical relations, and a second level of structures or schemas. Discourse parsers or other computer software can parse text into a discourse tree.
  • Rhetoric Structure Theory models logical organization of text, a structure employed by a writer, relying on relations between parts of text.
  • RST simulates text coherence by forming a hierarchical, connected structure of texts via discourse trees.
  • Rhetoric relations are split into the classes of coordinate and subordinate; these relations hold across two or more text spans and therefore implement coherence.
  • These text spans are called elementary discourse units (EDUs).
  • EEUs elementary discourse units
  • Clauses in a sentence and sentences in a text are logically connected by the author. The meaning of a given sentence is related to that of the previous and the following sentences. This logical relation between clauses is called the coherence structure of the text.
  • RST is one of the most popular theories of discourse, being based on a tree-like discourse structure, discourse trees (DTs).
  • the leaves of a DT correspond to EDUs, the contiguous atomic text spans.
  • Adjacent EDUs are connected by coherence relations (e.g., Attribution, Sequence), forming higher-level discourse units. These units are then also subject to this relation linking.
  • EDUs linked by a relation are then differentiated based on their relative importance: nuclei are the core parts of the relation, while satellites are peripheral ones. As discussed, in order to determine accurate request-response pairs, both topic and rhetorical agreement are analyzed.
  • a speaker When a speaker answers a question, such as a phrase or a sentence, the speaker’s answer should address the topic of this question.
  • a question such as a phrase or a sentence
  • the speaker’s answer should address the topic of this question.
  • an appropriate answer is expected not only maintain a topic, but also match the generalized epistemic state of this seed.
  • FIG. 2 depicts an example of a discourse tree in accordance with an aspect.
  • FIG. 2 includes discourse tree 200.
  • Discourse tree includes text span 201, text span 202, text span 203, relation 210 and relation 228.
  • the numbers in FIG. 2 correspond to the three text spans.
  • FIG. 3 corresponds to the following example text with three text spans numbered 1, 2, 3: [0098] 1. Honolulu, Hawaii will be site of the 2017 Conference on Hawaiian History
  • relation 210 or elaboration, describes the relationship between text span 201 and text span 202.
  • Relation 228 depicts the relationship, elaboration, between text span 203 and 204.
  • text spans 202 and 203 elaborate further on text span 201.
  • text span 1 is the nucleus.
  • Text spans 2 and 3 provide more detail about the conference.
  • FIG. 1 Given a goal of notifying readers of a conference, text span 1 is the nucleus. Text spans 2 and 3 provide more detail about the conference.
  • a horizontal number e.g., 1-3, 1, 2, 3 covers a span of text (possibly made up of further spans); a vertical line signals the nucleus or nuclei; and a curve represents a rhetoric relation (elaboration) and the direction of the arrow points from the satellite to the nucleus. If the text span only functions as a satellite and not as a nuclei, then deleting the satellite would still leave a coherent text. If from FIG. 2 one deletes the nucleus, then text spans 2 and 3 are difficult to understand.
  • FIG. 3 depicts a further example of a discourse tree in accordance with an aspect.
  • FIG. 3 includes components 301 and 302, text spans 305-307, relation 310 and relation 328.
  • Relation 310 depicts the relationship 310, enablement, between components 306 and 305, and 307, and 305.
  • FIG. 3 refers to the following text spans:
  • relation 328 depicts the relationship between entity 307 and 306, which is enablement.
  • FIG. 3 illustrates that while nuclei can be nested, there exists only one most nuclear text span.
  • Discourse trees can be generated using different methods.
  • a simple example of a method to construct a DT bottom up is:
  • Unit size may vary, depending on the goals of the analysis
  • Mann and Thompson also describe the second level of building block structures called schemas applications.
  • RST rhetoric relations are not mapped directly onto texts; they are fitted onto structures called schema applications, and these in turn are fitted to text.
  • Schema applications are derived from simpler structures called schemas (as shown by FIG. 4). Each schema indicates how a particular unit of text is decomposed into other smaller text units.
  • a rhetorical structure tree or DT is a hierarchical system of schema applications.
  • a schema application links a number of consecutive text spans, and creates a complex text span, which can in turn be linked by a higher-level schema application.
  • RST asserts that the structure of every coherent discourse can be described by a single rhetorical structure tree, whose top schema creates a span encompassing the whole discourse.
  • FIG. 4 depicts illustrative schemas in accordance with an aspect.
  • FIG. 4 shows a joint schema is a list of items consisting of nuclei with no satellites.
  • FIG. 4 depicts schemas 401-406.
  • Schema 401 depicts a circumstance relation between text spans 410 and 428.
  • Scheme 402 depicts a sequence relation between text spans 420 and 421 and a sequence relation between text spans 421 and 422.
  • Schema 403 depicts a contrast relation between text spans 430 and 431.
  • Schema 404 depicts a joint relationship between text spans 440 and 441.
  • Schema 405 depicts a motivation relationship between 450 and 451, and an enablement relationship between 452 and 451.
  • Schema 406 depicts joint relationship between text spans 460 and 462.
  • An example of a joint scheme is shown in FIG. 4 for the three text spans below:
  • FIGs. 2-4 depict some graphical representations of a discourse tree, other representations are possible.
  • FIG. 5 depicts a node-link representation of the hierarchical binary tree in accordance with an aspect.
  • the leaves of a DT correspond to contiguous non-overlapping text spans called Elementary Discourse Units (EDUs).
  • Adjacent EDUs are connected by relations (e.g., elaboration, attribution%) and form larger discourse units, which are also connected by relations.
  • “Discourse analysis in RST involves two sub- tasks: discourse segmentation is the task of identifying the EDUs, and discourse parsing is the task of linking the discourse units into a labeled tree.” See Joty, Shafiq R and Giuseppe Carenini, Raymond T Ng, and Yashar Mehdad. 2013. Combining intra-and multi- sentential rhetorical parsing for document-level discourse analysis.
  • ACL (1) pages 486-496.
  • FIG. 5 depicts text spans that are leaves, or terminal nodes, on the tree, each numbered in the order they appear in the full text, shown in FIG. 6.
  • FIG. 5 includes tree 500.
  • Tree 500 includes, for example, nodes 501-507.
  • the nodes indicate relationships. Nodes are non-terminal, such as node 501, or terminal, such as nodes 502-507. As can be seen, nodes 503 and 504 are related by a joint relationship. Nodes 502, 505, 506, and 508 are nuclei.
  • the dotted lines indicate that the branch or text span is a satellite.
  • the relations are nodes in gray boxes.
  • FIG. 6 depicts an exemplary indented text encoding of the representation in FIG. 5 in accordance with an aspect.
  • FIG. 6 includes text 600 and text sequences 602-604.
  • Text 600 is presented in a manner more amenable to computer programming.
  • Text sequence 602 corresponds to node 502
  • sequence 603 corresponds to node 503
  • sequence 604 corresponds to node 504.
  • N indicates a nucleus
  • S indicates a satellite.
  • Automatic discourse segmentation can be performed with different methods. For example, given a sentence, a segmentation model identifies the boundaries of the composite elementary discourse units by predicting whether a boundary should be inserted before each particular token in the sentence. For example, one framework considers each token in the sentence sequentially and independently. In this framework, the segmentation model scans the sentence token by token, and uses a binary classifier, such as a support vector machine or logistic regression, to predict whether it is appropriate to insert a boundary before the token being examined. In another example, the task is a sequential labeling problem. Once text is segmented into elementary discourse units, sentence-level discourse parsing can be performed to construct the discourse tree. Machine learning techniques can be used.
  • RST Rhetorical Structure Theory
  • NLP Natural Language Processing
  • CoreNLPProcessor and FastNLPProcessor use Natural Language Processing (NLP) for syntactic parsing.
  • NLP Natural Language Processing
  • the Stanford CoreNLP gives the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and syntactic dependencies, indicate which noun phrases refer to the same entities.
  • RST is a still theory that may work in many cases of discourse, but in some cases, it may not work.
  • the program's precision is 5/8 while its recall is 5/12.
  • a conversation between Human A and Human B is a form of discourse.
  • applications exist such as FaceBook® Messenger, WhatsApp®, Slack,® SMS, etc.
  • a conversation between A and B may typically be via messages in addition to more traditional email and voice conversations.
  • a chatbot (which may also be called intelligent hots or virtual assistant, etc.) is an “intelligent” machine that, for example, replaces human B and to various degrees mimics the conversation between two humans.
  • An example ultimate goal is that human A cannot tell whether B is a human or a machine (the Turning test, developed by Alan Turing in 1950).
  • Discourse analysis, artificial intelligence, including machine learning, and natural language processing have made great strides toward the long term goal of passing the Turing test.
  • the chatbot being human-like and a computer combined.
  • UI conversational user interface
  • NLP Natural language processing
  • ML machine learning
  • An intent at a high level is what the end user would like to accomplish (e.g., get account balance, make a purchase).
  • An intent is essentially, a mapping of customer input to a unit of work that the backend should perform. Therefore, based on the phrases uttered by the user in the chatbot, these are mapped that to a specific and discrete use case or unit of work, for e.g. check balance, transfer money and track spending are all “use cases” that the chatbot should support and be able to work out which unit of work should be triggered from the free text entry that the end user types in a natural language.
  • a request to perform some action there are typically two types of requests: (1) A request to perform some action; and (2) a request for information, e.g., a question.
  • the first type has a response in which a unit of work is created.
  • the second type has a response that is, e.g., a good answer, to the question.
  • the answer could take the form of, for example, in some aspects, the AI constructing an answer from its extensive knowledge base(s) or from matching the best existing answer from searching the internet or intranet or other publically/privately available data sources.
  • aspects of the present disclosure build communicative discourse trees and use communicative discourse trees to analyze whether the rhetorical structure of a request or question agrees with an answer. More specifically, aspects described herein create representations of a request-response pair, learns the representations, and relates the pairs into classes of valid or invalid pairs. In this manner, an autonomous agent can receive a question from a user, process the question, for example, by searching for multiple answers, determine the best answer from the answers, and provide the answer to the user.
  • aspects described herein use rhetoric relations and speech acts (or communicative actions).
  • Rhetoric relations are relationships between the parts of the sentences, typically obtained from a discourse tree.
  • Speech acts are obtained as verbs from a verb resource such as VerbNet.
  • aspects described herein can correctly recognize valid request-response pairs. To do so, aspects correlate the syntactic structure of a question with that of an answer. By using the structure, a better answer can be determined.
  • an autonomous agent receives an indication from a person that the person desires to sell an item with certain features, the autonomous agent should provide a search result that not only contains the features but also indicates an intent to buy.
  • the autonomous agent has determined the user’s intent.
  • the search result should contain an intent to receive a recommendation.
  • the autonomous agent shares an opinion about the subject, rather than soliciting another opinion.
  • FIG. 7 depicts an exemplary discourse tree for an example request about property tax in accordance with an aspect.
  • the node labels are the relations and the arrowed line points to the satellite.
  • the nucleus is a solid line.
  • FIG. 7 depicts the following text.
  • the main subject of the topic is “Property tax on a car”.
  • the question includes the contradiction: on one hand, all properties are taxable, and on the other hand, the ownership is somewhat incomplete.
  • a good response has to address both topic of the question and clarify the inconsistency. To do that, the responder is making even stronger claim concerning the necessity to pay tax on whatever is owned irrespectively of the registration status.
  • This example is a member of positive training set from our Yahoo! Answers evaluation domain.
  • the main subject of the topic is “Property tax on a car”.
  • the question includes the contradiction: on one hand, all properties are taxable, and on the other hand, the ownership is somewhat incomplete.
  • FIG. 8 depicts an exemplary response for the question represented in FIG. 7, according to certain aspects of the present invention.
  • the central nucleus is “the property tax is assessed on property” elaborated by “that you own”. “The property tax is assessed on property that you own” is also a nucleus elaborated by “Just because you chose to not register it does not mean that you don't own it, so the tax is not refundable. Even if you have not titled the vehicle yet, you still own it within the boundaries of the tax district, so the tax is payable. Note that all states give you a limited amount of time to transfer title and pay the use tax.”
  • FIG. 9 illustrates a discourse tree for an official answer in accordance with an aspect.
  • an official answer, or mission statement states that “The Investigative Committee of the Russian Federation is the main federal investigating authority which operates as Russia's Anti-corruption agency and has statutory responsibility for inspecting the police forces, combating police corruption and police misconduct, is responsible for conducting investigations into local authorities and federal governmental bodies.”
  • FIG. 10 illustrates a discourse tree for a raw answer in accordance with an aspect.
  • another, perhaps more honest, answer states that “Investigative Committee of the Russian Federation is supposed to fight corruption.
  • Top-rank officers of the Investigative Committee of the Russian Federation are charged with creation of a criminal community. Not only that, but their involvement in large bribes, money laundering, obstruction of justice, abuse of power, extortion, and racketeering has been reported. Due to the activities of these officers, dozens of high-profile cases including the ones against criminal lords had been ultimately ruined.”
  • Application 102 can determine whether a given answer or response, such as an answer obtained from answer database 105 or a public database, is responsive to a given question, or request. More specifically, application 102 analyzes whether a request and response pair is correct or incorrect by determining one or both of (i) relevance or (ii) rhetoric agreement between the request and the response. Rhetoric agreement can be analyzed without taking into account relevance, which can be treated orthogonally.
  • Application 102 can determine similarity between question-answer pairs using different methods. For example, application 102 can determine level of similarity between an individual question and an individual answer. Alternatively, application 102 can determine a measure of similarity between a first pair including a question and an answer, and a second pair including a question and answer.
  • application 102 uses rhetoric agreement classifier 120 trained to predict matching or non-matching answers.
  • Application 102 can process two pairs at a time, for example ⁇ ql, al > and ⁇ q2, a2 >.
  • Application 102 compares ql with q2 and al with al, producing a combined similarity score. Such a comparison allows a determination of whether an unknown question/answer pair contains a correct answer or not by assessing a distance from another question/answer pair with a known label.
  • an unlabeled pair ⁇ q2, a2 > can be processed so that rather than “guessing” correctness based on words or structures shared by q2 and a2, both q2 and a2 can be compared with their corresponding components ql and a2 of the labeled pair ⁇ q2, a2 > on the grounds of such words or structures. Because this approach targets a domain-independent classification of an answer, only the structural cohesiveness between a question and answer can be leveraged, not ‘meanings’ of answers.
  • application 102 uses training data 125 to train rhetoric agreement classifier 120.
  • rhetoric agreement classifier 120 is trained to determine a similarity between pairs of questions and answers.
  • This is a classification problem.
  • Training data 125 can include a positive training set and a negative training set.
  • Training data 125 includes matching request-response pairs in a positive dataset and arbitrary or lower relevance or appropriateness request-response pairs in a negative dataset. For the positive dataset, various domains with distinct acceptance criteria are selected that indicate whether an answer or response is suitable for the question.
  • Each training data set includes a set of training pairs.
  • Each training set includes a question communicative discourse tree that represents a question and an answer communicative discourse tree that represents an answer and an expected level of complementarity between the question and answer.
  • application 102 provides a training pair to rhetoric agreement classifier 120 and receives, from the model, a level of complementarity.
  • Application 102 calculates a loss function by determining a difference between the determined level of complementarity and an expected level of complementarity for the particular training pair. Based on the loss function, application 102 adjusts internal parameters of the classification model to minimize the loss function.
  • Acceptance criteria can vary by application. For example, acceptance criteria may be low for community question answering, automated question answering, automated and manual customer support systems, social network communications and writing by individuals such as consumers about their experience with products, such as reviews and complaints. RR acceptance criteria may be high in scientific texts, professional journalism, health and legal documents in the form of FAQ, professional social networks such as “stackoverflow ”
  • CDTs Communicative Discourse Trees
  • Application 102 can create, analyze, and compare communicative discourse trees.
  • Communicative discourse trees are designed to combine rhetoric information with speech act structures.
  • CDTs include with arcs labeled with expressions for communicative actions. By combining communicative actions, CDTs enable the modeling of RST relations and communicative actions.
  • a CDT is a reduction of a parse thicket. See Galitsky, B, Ilvovsky, D. and Kuznetsov SO. Rhetoric Map of an Answer to Compound Queries Knowledge Trail Inc. ACL 2015, 681-686. (“Galitsky 2015”).
  • a parse thicket is a combination of parse trees for sentences with discourse-level relationships between words and parts of the sentence in one graph. By incorporating labels that identify speech actions, learning of communicative discourse trees can occur over a richer features set than just rhetoric relations and syntax of elementary discourse units (EDUs).
  • EEUs elementary discourse units
  • FIG. 11 illustrates a communicative discourse tree for a claim of a first agent in accordance with an aspect.
  • FIG. 11 depicts communicative discourse tree 100, which represents the following text: “Dutch accident investigators say that evidence points to pro- Russian rebels as being responsible for shooting down plane. The report indicates where the missile was fired from and identifies who was in control of the territory and pins the downing of MH17 on the pro-Russian rebels.”
  • non-terminal nodes of CDTs are rhetoric relations, and terminal nodes are elementary discourse units (phrases, sentence fragments) which are the subjects of these relations.
  • Certain arcs of CDTs are labeled with the expressions for communicative actions, including the actor agent and the subject of these actions (what is being communicated).
  • the nucleus node for elaboration relation on the left
  • the satellite with responsible(rebels, shooting down) are labeled with say (Dutch, evidence), and the satellite with responsible(rebels, shooting down).
  • curvy arcs are discourse relations, such as anaphora, same entity, sub-entity, rhetoric relation and communicative actions.
  • This graph includes much richer information than just a combination of parse trees for individual sentences.
  • parse thickets can be generalized at the level of words, relations, phrases and sentences.
  • the speech actions are logic predicates expressing the agents involved in the respective speech acts and their subjects.
  • the arguments of logical predicates are formed in accordance to respective semantic roles, as proposed by a framework such as VerbNet.
  • FIG. 12 illustrates a communicative discourse tree for a claim of a second agent in accordance with an aspect.
  • FIG. 12 depicts communicative discourse tree 1200, which represents the following text: “The Investigative Committee of the Russian Federation believes that the plane was hit by a missile, which was not produced in Russia. The committee cites an investigation that established the type of the missile.”
  • FIG. 13 illustrates a communicative discourse tree for a claim of a third agent in accordance with an aspect.
  • FIG. 13 depicts communicative discourse tree 1300, which represents the following text: “Rebels, the self-proclaimed Donetsk People's Republic, deny that they controlled the territory from which the missile was allegedly fired. It became possible only after three months after the tragedy to say if rebels controlled one or another town.”
  • a response is not arbitrary.
  • a response talks about the same entities as the original text.
  • communicative discourse trees 1200 and 1300 are related to communicative discourse tree 1100.
  • a response backs up a disagreement with estimates and sentiments about these entities, and about actions of these entities.
  • replies of involved agent need to reflect the communicative discourse of the first, seed message.
  • the other agents have to follow the suite and either provide their own attributions or attack the validity of attribution of the proponent, or both.
  • pairs of respective CDTs can be learned.
  • Computational verb lexicons help support acquisition of entities for actions and provide a rule-based form to express their meanings.
  • Verbs express the semantics of an event being described as well as the relational information among participants in that event, and project the syntactic structures that encode that information.
  • Verbs, and in particular communicative action verbs can be highly variable and can display a rich range of semantic behaviors.
  • verb classification helps a learning systems to deal with this complexity by organizing verbs into groups that share core semantic properties.
  • VerbNet is one such lexicon, which identifies semantic roles and syntactic patterns characteristic of the verbs in each class and makes explicit the connections between the syntactic patterns and the underlying semantic relations that can be inferred for all members of the class. See Karin Kipper, Anna Korhonen, Neville Ryant and Martha Palmer, Language Resources and Evaluation, Vol. 42, No. 1 (March 2008), at 21. Each syntactic frame, or verb signature, for a class has a corresponding semantic representation that details the semantic relations between event participants across the course of the event.
  • the verb amuse is part of a cluster of similar verbs that have a similar structure of arguments (semantic roles) such as intimid, anger, arouse, disturb, and irritate.
  • the roles of the arguments of these communicative actions are as follows: Experiencer (usually, an animate entity), Stimulus, and Result.
  • Each verb can have classes of meanings differentiated by syntactic features for how this verb occurs in a sentence, or frames.
  • the frames for amuse are as follows, using the following key noun phrase (NP), noun (N), communicative action (V), verb phrase (VP), adverb (ADV):
  • NP V NP Example: "The teacher amused the children.” Syntax: Stimulus V Experiencer. Clause: amuse(Stimulus, E, Emotion, Experiencer), cause(Stimulus, E), emotional_state(result(E), Emotion, Experiencer).
  • NP V NP-PRO-ARB Example "The teacher amused.” Syntax Stimulus V. amuse(Stimulus, E, Emotion, Experiencer):. cause(Stimulus, E), emotional_state(result(E), Emotion, Experiencer).
  • NP. cause V NP Example "The teacher's dolls amused the children.” syntax Stimulus ⁇ +genitive> ('s) V Experiencer. amuse(Stimulus, E, Emotion, Experiencer):. cause(Stimulus, E), emotional_state(during(E), Emotion, Experiencer). [0184] NP V NP ADJ. Example "This performance bored me totally.” syntax Stimulus V
  • Communicative actions can be characterized into clusters, for example:
  • Verbs with Predicative Complements (Appoint, characterize, dub, declare, conjecture, masquerade, orphan, captain, consider, classify), Verbs of Perception (See, sight, peer).
  • Judgment Verbs Judgment
  • Verbs of Assessment Assess, estimate
  • Verbs of Searching Hunt, search, stalk, investigate, rummage, ferret
  • Verbs of Social Interaction Correspond, marry, meet, battle
  • Verbs of Communication Transfer(message)
  • Avoid Verbs Avoid), Measure Verbs, (Register, cost, fit, price, bill), Aspectual Verbs (Begin, complete, continue, stop, establish, sustain.
  • aspects described herein provide advantages over statistical learning models.
  • aspects use a classification system can provide a verb or a verb-like structure which is determined to cause the target feature (such as rhetoric agreement).
  • statistical machine learning models express similarity as a number, which can make interpretation difficult.
  • request-response pairs facilitates classification based operations based on a pair.
  • request-response pairs can be represented as parse thickets.
  • a parse thicket is a representation of parse trees for two or more sentences with discourse-level relationships between words and parts of the sentence in one graph. See Galitsky 2015. Topical similarity between question and answer can expressed as common sub-graphs of parse thickets. The higher the number of common graph nodes, the higher the similarity.
  • FIG. 14 illustrates parse thickets in accordance with an aspect.
  • FIG. 14 depicts parse thicket 1400 including a parse tree for a request 1401, and a parse tree for a corresponding response 1402.
  • Parse tree 1401 represents the question “I just had a baby and it looks more like the husband I had my baby with. However it does not look like me at all and I am scared that he was cheating on me with another lady and I had her kid. This child is the best thing that has ever happened to me and I cannot imagine giving my baby to the real mom.”
  • Response 1402 represents the response “Marital therapists advise on dealing with a child being born from an affair as follows. One option is for the husband to avoid contact but just have the basic legal and financial commitments. Another option is to have the wife fully involved and have the baby fully integrated into the family just like a child from a previous marriage.”
  • FIG. 14 represents a greedy approach to representing linguistic information about a paragraph of text.
  • curvy arcs are discourse relations, such as anaphora, same entity, sub-entity, rhetoric relation and communicative actions.
  • the solid arcs are for same entity/sub-entity/anaphora relations, and the dotted arcs are for rhetoric relations and communicative actions.
  • Oval labels in straight edges denote the syntactic relations. Lemmas are written in the boxes for the nodes, and lemma forms are written on the right side of the nodes.
  • Parse thicket 1400 includes much richer information than just a combination of parse trees for individual sentences. Navigation through this graph along the edges for syntactic relations as well as arcs for discourse relations allows to transform a given parse thicket into semantically equivalent forms for matching with other parse thickets, performing a text similarity assessment task. To form a complete formal representation of a paragraph, as many links as possible are expressed. Each of the discourse arcs produces a pair of thicket phrases that can be a potential match.
  • Topical similarity between the seed (request) and response is expressed as common sub-graphs of parse thickets. They are visualized as connected clouds. The higher the number of common graph nodes, the higher the similarity. For rhetoric agreement, common sub graph does not have to be large as it is in the given text. However, rhetoric relations and communicative actions of the seed and response are correlated and a correspondence is required.
  • a similarity between two communicative actions A 1 and A 2 is defined as a an abstract verb which possesses the features which are common between A 1 and A 2.
  • Defining a similarity of two verbs as an abstract verb-like structure supports inductive learning tasks, such as a rhetoric agreement assessment.
  • Proposed action is an action that the Speaker would perform if they were to accept or refuse the request or offer, and The Speaker is the person to whom a particular action has been proposed and who responds to the request or offer made.
  • the subjects of communicative actions are generalized in the context of communicative actions and are not be generalized with other “physical” actions. Hence, aspects generalize individual occurrences of communicative actions together with corresponding subjects.
  • sequences of communicative actions representing dialogs can be compared against other such sequences of similar dialogs. In this manner, the meaning of an individual communicative action as well as the dynamic discourse structure of a dialogue is (in contrast to its static structure reflected via rhetoric relations) is represented.
  • a generalization is a compound structural representation that happens at each level. Lemma of a communicative action is generalized with lemma, and its semantic role are generalized with respective semantic role.
  • communicative actions can also be thought of from the standpoint of matching the verb frames, such as VerbNet.
  • the communicative links reflect the discourse structure associated with participation (or mentioning) of more than a single agent in the text.
  • the links form a sequence connecting the words for communicative actions (either verbs or multi-words implicitly indicating a communicative intent of a person).
  • Communicative actions include an actor, one or more agents being acted upon, and the phrase describing the features of this action.
  • a communicative action can be described as a function of the form: verb (agent, subject, cause), where verb characterizes some type of interaction between involved agents (e.g., explain, confirm, remind, disagree, deny, etc.), subject refers to the information transmitted or object described, and cause refers to the motivation or explanation for the subject.
  • Each arc action;, actiori j E A cause corresponds to an attack relationship between action; and action j indicating that the cause of action; in conflict with the subject or cause of action j.
  • Subgraphs of parse thickets associated with scenarios of interaction between agents have some distinguishing features. For example, (1) all vertices are ordered in time, so that there is one incoming arc and one outgoing arc for all vertices (except the initial and terminal vertices), (2) for A sequence arcs, at most one incoming and only one outgoing arc are admissible, and (3) for A cause arcs, there can be many outgoing arcs from a given vertex, as well as many incoming arcs.
  • the vertices involved may be associated with different agents or with the same agent (i.e., when he contradicts himself).
  • induced subgraphs the sub-graphs of the same configuration with similar labels of arcs and strict correspondence of vertices are analyzed.
  • Some relations between discourse trees can be generalized, such as arcs that represent the same type of relation (presentation relation, such as antithesis, subject matter relation, such as condition, and multinuclear relation, such as list) can be generalized.
  • a nucleus or a situation presented by a nucleus is indicated by “N ” Satellite or situations presented by a satellite, are indicated by “S ” “W” indicates a writer.
  • “R” indicates a reader (hearer).
  • Situations are propositions, completed actions or actions in progress, and communicative actions and states (including beliefs, desires, approve, explain, reconcile and others).
  • the RST relation part is empty.
  • the expressions that are the verbal definitions of respective RST relations are then generalized. For example, for each word or a placeholder for a word such as an agent, this word (with its POS) is retained if the word the same in each input phrase or remove the word if the word is different between these phrases.
  • the resultant expression can be interpreted as a common meaning between the definitions of two different RST relations, obtained formally.
  • Two arcs between the question and the answer depicted in FIG. 14 show the generalization instance based on the RST relation “RST-contrast”. For example, “I just had a baby” is a RST-contrast with “it does not look like me,” and related to “husband to avoid contact” which is a RST-contrast with “have the basic legal and financial commitments.”
  • the answer need not have to be similar to the verb phrase of the question but the rhetoric structure of the question and answer are similar. Not all phrases in the answer must match phrases in question. For example, the phrases that do not match have certain rhetoric relations with the phrases in the answer which are relevant to phrases in question.
  • FIG. 15 illustrates an exemplary process for building a communicative discourse tree in accordance with an aspect.
  • Application 102 can implement process 1500.
  • communicative discourse trees enable improved search engine results.
  • process 1500 involves accessing a sentence comprising fragments. At least one fragment includes a verb and words and each word includes a role of the words within the fragment, and each fragment is an elementary discourse unit.
  • application 102 accesses a sentence such as “Rebels, the self-proclaimed Donetsk People's Republic, deny that they controlled the territory from which the missile was allegedly fired” as described with respect to FIG. 13.
  • application 102 determines that the sentence includes several fragments. For example, a first fragment is “rebels .. deny.” A second fragment is “that they controlled the territory.” A third fragment is “from which the missile was allegedly fired.” Each fragment includes a verb, for example, “deny” for the first fragment and “controlled” for the second fragment. Although, a fragment need not include a verb.
  • process 1500 involves generating a discourse tree that represents rhetorical relationships between the sentence fragments.
  • the discourse tree including nodes, each nonterminal node representing a rhetorical relationship between two of the sentence fragments and each terminal node of the nodes of the discourse tree is associated with one of the sentence fragments.
  • application 102 generates a discourse tree as shown in FIG. 13.
  • the third fragment “from which the missile was allegedly fired” elaborates on “that they controlled the territory.”
  • the second and third fragments together relate to attribution of what happened, i.e., the attack cannot have been the rebels because they do not control the territory.
  • process 1500 involves accessing multiple verb signatures.
  • application 102 accesses a list of verbs, e.g., from VerbNet. Each verb matches or is related to the verb of the fragment. For example, the for the first fragment, the verb is “deny.” Accordingly, application 102 accesses a list of verb signatures that relate to the verb deny.
  • each verb signature includes the verb of the fragment and one or more of thematic roles.
  • a signature includes one or more of noun phrase (NP), noun (N), communicative action (V), verb phrase (VP), or adverb (ADV).
  • the thematic roles describing the relationship between the verb and related words. For example “the teacher amused the children” has a different signature from “small children amuse quickly.”
  • the verb “deny,” application 102 accesses a list of frames, or verb signatures for verbs that match “deny.” The list is “NP V NP to be NP,” “NP V that S” and “NP V NP ”
  • Each verb signature includes thematic roles.
  • a thematic role refers to the role of the verb in the sentence fragment.
  • Application 102 determines the thematic roles in each verb signature.
  • Example thematic roles include actor, agent, asset, attribute, beneficiary, cause, location destination source, destination, source, location, experiencer, extent, instrument, material and product, material, product, patient, predicate, recipient, stimulus, theme, time, or topic.
  • process 1500 involves determining, for each verb signature of the verb signatures, a number of thematic roles of the respective signature that match a role of a word in the fragment. For the first fragment, rhetorical classification application 102 determines that the verb “deny” has only three roles, “agent”, “verb” and “theme.”
  • process 1500 involves selecting a particular verb signature from the verb signatures based on the particular verb signature having a highest number of matches. For example, referring again to FIG. 13, deny in the first fragment “the rebels deny... that they control the territory” is matched to verb signature deny “NP V NP”, and “control” is matched to control (rebel, territory). Verb signatures are nested, resulting in a nested signature of “deny(rebel, control (rebel, territory)).”
  • Request-response pairs can be analyzed alone or as pairs.
  • request- response pairs can be chained together.
  • rhetoric agreement is expected to hold not only between consecutive members but also triples and four-tuples.
  • a discourse tree can be constructed for a text expressing a sequence of request-response pairs. For example, in the domain of customer complaints, request and response are present in the same text, from the viewpoint of a complainant. Customer complaint text can to be split into request and response text portions and then form the positive and negative dataset of pairs. In an example, all text for the proponent and all text for the opponent is combined. The first sentence of each paragraph below will form the Request part (which will include three sentences) and second sentence of each paragraph will form the Response part (which will also include three sentences in this example).
  • FIG. 16 illustrates a discourse tree and scenario graph in accordance with an aspect.
  • FIG. 16 depicts discourse tree 1601 and scenario graph 1602.
  • Discourse tree 1601 corresponds to the following three sentences:
  • POS part-of-speech
  • search in a conventional search, as a baseline, the match between request response pairs can be measured in terms of keyword statistics such as short for term frequency-inverse document frequency (TF*IDF). To improve search relevance, this score is augmented by item popularity, item location or taxonomy -based score (Galitsky 2015). Search can also be formulated as a passage re-ranking problem in machine learning framework.
  • the feature space includes request-response pairs as elements, and a separation hyper-plane splits this feature space into correct and incorrect pairs.
  • a search problem can be formulated in a local way, as similarity between Req and Resp, or in a global, learning way, via similarity between request-response pairs.
  • application 102 extracts features for Req and Resp and compares the features as a count, introducing a scoring function such that a score would indicate a class (low score for incorrect pairs, high score for correct ones)
  • application 102 compares representations for Req and Resp against each other, and assigns a score for the comparison result. Analogously, the score will indicate a class.
  • application 102 builds a representation for a pair Req and Resp, ⁇ Req, Resp> as elements of training set. Application 102 then performs learning in the feature space of all such elements ⁇ Req, Resp>.
  • FIG. 17 illustrates forming a request-response pair in accordance with an aspect.
  • FIG. 17 depicts request-response pair 1701, request tree (or object) 1702, and response tree 1703.
  • the application 102 combines the discourse tree for the request and the discourse tree for the response into a single tree with the root RR.
  • the application 102 then classifies the objects into correct (with high agreement) and incorrect (with low agreement) categories.
  • an ordered set G of CDTs(V,E) with vertex- and edge-labels from the sets (L V , ⁇ ) and (AE, ⁇ ) is constructed.
  • a labeled CDT G from G is a pair of pairs of the form ((V,l),(E,b)), where V is a set of vertices, E is a set of edges, 1: V L V is a function assigning labels to vertices, and b: E A E is a function assigning labels to edges. Isomorphic trees with identical labeling are not distinguished.
  • This definition takes into account the calculation of similarity (“weakening”) of labels of matched vertices when passing from the “larger” CDT Gi to “smaller” CDT G2.
  • FIG. 18 illustrates a maximal common sub-communicative discourse tree in accordance with an aspect. Notice that the tree is inverted and the labels of arcs are generalized: Communicative action site() is generalized with communicative action say(). The first (agent) argument of the former CA committee is generalized with the first argument of the latter CA Dutch. The same operation is applied to the second arguments for this pair of CAs: investigator L evidence.
  • CDT U belongs to a positive class such that (1) U is similar to (has a nonempty common sub-CDT) with a positive example R + and (2) for any negative example R , if U is similar to R (i.e., U * R10) then U * R m U * R + .
  • This condition introduces the measure of similarity and says that to be assigned to a class, the similarity between the unknown CDT U and the closest CDT from the positive class should be higher than the similarity between U and each negative example.
  • Condition 2 implies that there is a positive example R + such that for no R one has U * R + m R, i.e., there is no counterexample to this generalization of positive examples.
  • Tree Kernel learning for strings, parse trees and parse thickets is a well-established research area these days.
  • the parse tree kernel counts the number of common sub-trees as the discourse similarity measure between two instances.
  • Tree kernel has been defined for DT by Joty, Shafiq and A. Moschitti. Discriminative Reranking of Discourse Parses Using Tree Kernels. Proceedings of EMNLP. (2014). See also Wang, W., Su, T, & Tan, C. L. (2010). Kernel Based Discourse Relation Recognition with Temporal Ordering Information. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (using the special form of tree kernels for discourse relation recognition).
  • a thicket kernel is defined for a CDT by augmenting a DT kernel by the information on communicative actions.
  • a CDT can be represented by a vector V of integer counts of each sub-tree type (without taking into account its ancestors):
  • V (T) ( #of subtrees of type 1, ... , # of subtrees of type /, ... , # of subtrees of type n). This results in a very high dimensionality since the number of different sub-trees is exponential in its size. Thus, it is computational infeasible to directly use the feature vector 0(T). To solve the computational issue, a tree kernel function is introduced to calculate the dot product between the above high dimensional vectors efficiently. Given two tree segments CDT1 and CDT2 , the tree kernel function is defined:
  • K (CDT1, CDT2) is an instance of convolution kernels over tree structures (Collins and Duffy, 2002) and can be computed by recursive definitions:
  • D (nl, n2) 0 if nl and n2 are assigned the same POS tag or their children are different subtrees.
  • the recursive rule (3) holds because given two nodes with the same children, one can construct common sub-trees using these children and common sub-trees of further offspring.
  • the parse tree kernel counts the number of common sub-trees as the syntactic similarity measure between two instances.
  • FIG. 19 illustrates a tree in a kernel learning format for a communicative discourse tree in accordance with an aspect.
  • the terms for Communicative Actions as labels are converted into trees which are added to respective nodes for RST relations.
  • the terminal nodes are labeled with the sequence of phrase types instead of parse tree fragments.
  • Rhetoric agreement classifier 120 can determine the complementarity between two sentences, such as a question and an answer, by using communicative discourse trees.
  • FIG. 20 illustrates an exemplary process used to implement a rhetoric agreement classifier in accordance with an aspect.
  • FIG. 20 depicts process 2000, which can be implemented by application 102.
  • rhetoric agreement classifier 120 is trained with training data 125.
  • Rhetoric agreement classifier 120 determines a communicative discourse tree for both question and answer. For example, rhetoric agreement classifier 120 constructs question communicative discourse tree from a question such as question 171 or input text 130, and answer communicative discourse tree from a candidate answer.
  • process 2000 involves determining, for a question sentence, a question communicative discourse tree including a question root node.
  • a question sentence can be an explicit question, a request, or a comment.
  • Application 102 creates question communicative discourse tree from input text 130. Using the example discussed in relation to FIGs. 13 and 15, an example question sentence is “are rebels responsible for the downing of the flight.” Application 102 can use process 1500 described with respect to FIG. 15. The example question has a root node of “elaborate.”
  • process 2000 involves determining, for an answer sentence, a second communicative discourse tree, wherein the answer communicative discourse tree includes an answer root node.
  • application 102 creates an communicative discourse tree 111, as depicted in FIG. 13, which also has a root node “elaborate.”
  • process 2000 involves associating the communicative discourse trees by identifying that the question root node and the answer root node are identical.
  • Application 102 determines that the question communicative discourse tree and answer communicative discourse tree have an identical root node.
  • the resulting associated communicative discourse tree is depicted in FIG. 17 and can be labeled as a “request- response pair.”
  • process 2000 involves computing a level of complementarity between the question communicative discourse tree and the answer communicative discourse tree by applying a predictive model to the merged discourse tree.
  • the rhetoric agreement classifier uses machine learning techniques.
  • the application 102 trains and uses rhetoric agreement classifier 120.
  • application 102 defines positive and negative classes of request-response pairs.
  • the positive class includes rhetorically correct request-response pairs and the negative class includes relevant but rhetorically foreign request-response pairs.
  • the application 102 For each request-response pair, the application 102 builds a CDT by parsing each sentence and obtaining verb signatures for the sentence fragments. [0257] Application 102 provides the associated communicative discourse tree pair to rhetoric agreement classifier 120. Rhetoric agreement classifier 120 outputs a level of complementarity.
  • process 2000 involves responsive to determining that the level of complementarity is above a threshold, identifying the question and answer sentences as complementary.
  • Application 102 can use a threshold level of complementarity to determine whether the question-answer pair is sufficiently complementary. For example, if a classification score is greater than a threshold, then application 102 can output the answer as answer 172 or answer 150. Alternatively, application 102 can discard the answer and access answer database 105 or a public database for another candidate answer and repeat process 2000 as necessary.
  • the application 102 obtains co-references. In a further aspect, the application 102 obtains entity and sub-entity, or hyponym links. A hyponym is a word of more specific meaning than a general or superordinate term applicable to the word. For example, “spoon” is a hyponym of “cutlery ” [0260] In another aspect, application 102 applies thicket kernel learning to the representations. Thicket kernel learning can take place in place of classification-based learning described above, e.g., at block 2004. The application 102 builds a parse thicket pair for the parse tree of the request-response pair.
  • the application 102 applies discourse parsing to obtain a discourse tree pair for the request-response pair.
  • the application 102 aligns elementary discourse units of the discourse tree request-response and the parse tree request- response.
  • the application 102 merges the elementary discourse units of the discourse tree request-response and the parse tree request-response.
  • application 102 improves the text similarity assessment by word2vector model.
  • application 102 sends a sentence that corresponds to the question communicative discourse tree or a sentence that corresponds to the answer communicative discourse tree to a device such as mobile device 170.
  • Outputs from application 102 can be used as inputs to search queries, database lookups, or other systems. In this manner, application 102 can integrate with a search engine system.
  • FIG. 21 illustrates a chat hot commenting on a posting in accordance with an aspect.
  • FIG. 21 depicts chat 2100, user messages 2101-2104, and agent response 2105.
  • Agent response 2105 can be implemented by the application 102. As shown, agent response 2105 has identified a suitable answer to the thread of user messages 2101-2104.
  • FIG. 22 illustrates a chat hot commenting on a posting in accordance with an aspect.
  • FIG. 22 depicts chat 2200, user messages 2201-2205, and agent response 2206.
  • FIG. 22 depicts three messages from user 1, specifically 2201, 2203, and 2205, and two messages from user 2, specifically 2202 and 2204.
  • Agent response 2206 can be implemented by the application 102. As shown, agent response 2106 has identified a suitable answer to the thread of messages 2201-2204.
  • FIGs. 21 and 22 can be implemented by computing device 101, or by a device that provides input text 130 to computing device 101 and receives answer 150 from computing device 101.
  • the correspondence bias is the tendency for people to over-emphasize personality- based explanations for behaviors observed in others, responding to questions. See Bauhoff, R. F. & Bushman, B. J. Social psychology and human nature: International Edition. (2010). At the same time, those responding queries under-emphasize the role and power of situational influences on the same behavior.
  • Confirmation bias the inclination to search for or interpret information in a way that confirms the preconceptions of those answering questions. They may discredit information that does not support their views.
  • the confirmation bias is related to the concept of cognitive dissonance. Whereby, individuals may reduce inconsistency by searching for information which re-confirms their views.
  • Anchoring leads to relying too heavily, or “anchor”, on one trait or piece of information when making decisions.
  • Availability heuristic makes us overestimate the likelihood of events with greater "availability" in memory, which can be influenced by how recent the memories are or how unusual or emotionally charged they may be.
  • Belief bias is an effect where someone's evaluation of the logical strength of an argument is biased by the believability of the conclusion.
  • Bias blind spot is the tendency to see oneself as less biased than other people, or to be able to identify more cognitive biases in others than in oneself.
  • a first domain of test data is derived from question-answer pairs from Yahoo! Answers, which is a set of question-answer pairs with broad topics. Out of the set of 4.4 million user questions, 20000 are selected that each include more than two sentences. Answers for most questions are fairly detailed so no filtering was applied to answers. There are multiple answers per questions and the best one is marked.
  • Question-Best Answer as an element of the positive training set
  • Question-Other-Answer as the one of the negative training set.
  • To derive the negative set we either randomly select an answer to a different but somewhat related question, or formed a query from the question and obtained an answer from web search results.
  • Our second dataset includes the social media.
  • Request-Response pairs mainly from postings on Facebook.
  • the standards of writing are fairly low.
  • the cohesiveness of text is very limited and the logical structure and relevance frequently absent.
  • the authors formed the training sets from their own accounts and also public Facebook accounts available via API over a number of years (at the time of writing Facebook API for getting messages is unavailable).
  • the third domain is customer complaints.
  • a dissatisfied customer describes his problems with products and service as well as the process for how he attempted to communicate these problems with the company and how they responded.
  • Complaints are frequently written in a biased way, exaggerating product faults and presenting the actions of opponents as unfair and inappropriate.
  • the complainants try to write complaints in a convincing, coherent and logically consistent way (Galitsky 2014); therefore complaints serve as a domain with high agreement between requests and response.
  • For the purpose of assessing agreement between user complaint and company response (according to how this user describes it) we collected 670 complaints from planetfeedback.com over 10 years.
  • the fourth domain is interview by journalist. Usually, the way interviews are written by professional journalists is such that the match between questions and answers is very high. We collected 1200 contributions of professional and citizen journalists from such sources as datran.com, allvoices.com, huffmgtonpost.com and others.
  • Answer classification accuracies are shown in Table 1. Each row represents a particular method; each class of methods in shown in grayed areas.
  • SVM TK For statistical family of approaches (bottom 5 rows, tree kernels), the richest source of discourse data (SVM TK for RR-DT) gives the highest classification accuracy, almost the same as the RR similarity-based classification. Although SVM TK for RST and CA (full parse trees) included more linguistic data, some part of it (most likely, syntactic) is redundant and gives lower results for the limited training set. Using additional features under TK such as sentiment and argumentation does not help either: most likely, these features are derived from RR-CDT features and do not contribute to classification accuracy on their own.
  • the RR pair validity recognition framework can serve as a measure of agreement between an arbitrary request and response. Also, this recognition framework can assess how strongly various features are correlated with RR pair validity. [0290] From the evaluation of recognition accuracy, we obtained the best method to recognize of the RR pair is valid or not. Now, having this recognition method fixed, we will measure RR agreements in our evaluation domains, and will also estimate how a general, total agreement delivered by the best method is correlated with individual agreement criteria such as sentiment, logical argumentation, topics and keyword relevance. Once we use our best approach (SVM TK for RR-CDT) for labeling training set, the size of it can grow dramatically and we can explore interesting properties of RR agreement in various domains.
  • SVM TK for RR-CDT
  • Agreement by sentiment shows the contribution of proper sentiment match in RR pair.
  • the sentiment rule includes, in particular, that if the polarity of RR is the same, response should confirm what request is saying. Conversely, if polarity is opposite, response should attack what request is claiming. Agreement by logical argumentation requires proper communication discourse where a response disagrees with the claim in request.
  • a Conversational Agent for Social Promotion is an agent that is presented as a simulated human character which acts on behalf of its human host to facilitate and manage her communication for him or her.
  • Galitsky B. Dmitri Ilvovsky, Nina Lebedeva and Daniel Usikov. Improving Trust in Automation of Social Promotion. AAAI Spring Symposium on The Intersection of Robust Intelligence and Trust in Autonomous Systems Stanford CA 2014.
  • the CASP relieves its human host from the routine, less important activities on social networks such as sharing news and commenting on messages, blogs, forums, images and videos of others.
  • Conversational Agent for Social Promotion evolves with possible loss of trust. The overall performance of CASP with the focus on RR pair agreement, filtering replies mined from the web is evaluated.
  • CASP tracks user chats, user postings on blogs and forums, comments on shopping sites, and suggest web documents and their snippets, relevant to a purchase decisions. To do that, it needs to take portions of text, produce a search engine query, run it against a search engine API such as Bing, and filter out the search results which are determined to be irrelevant to a seed message. The last step is critical for a sensible functionality of CASP, and poor relevance in rhetoric space would lead to lost trust in it. Hence an accurate assessment of RR agreement is critical to a successful use of CASP.
  • CASP is presented as a simulated character that acts on behalf of its human host to facilitate and manage her communication for her (FIGs. 21-22).
  • the agent is designed to relieve its human host from the routine, less important activities on social networks such as sharing news and commenting on messages, blogs, forums, images and videos of others.
  • its social partners do not necessarily know that they exchange news, opinions, and updates with an automated agent.
  • CASP we experimented with CASP’s rhetoric agreement and reasoning about mental states of its peers in a number of Facebook accounts.
  • users For a conversational system, users need to feel that it properly reacts to their actions, and that what it replied makes sense. To achieve this in a horizontal domain, one needs to leverage linguistic information to a full degree to be able to exchange messages in a meaningful manner.
  • CASP inputs a seed (a posting written by a human) and outputs a message it forms from a content mined on the web and adjusted to be relevant to the input posting. This relevance is based on the appropriateness in terms of content and appropriateness in terms RR agreement, or a mental state agreement (for example, it responds by a question to a question, by an answer to a recommendation post seeking more questions, etc.).
  • FIGs. 21-22 illustrate a chat hot commenting on a posting.
  • Dijkstra a Dutch computer scientist who invented the concept of "structured programming”
  • the visionary was definitely right -the specialization and the high accuracy of programming languages are what made possible the tremendous progress in the computing and computers as well.
  • Dijkstra compares the invention of programming languages with invention of mathematical symbolism. In his words “Instead of regarding the obligation to use formal symbols as a burden, we should regard the convenience of using them as a privilege: thanks to them, school children can learn to do what in earlier days only genius could achieve”.
  • FIG. 23 illustrates a discourse tree for algorithm text in accordance with an aspect. We have the following text and its DT (FIG. 23):
  • FIG. 24 illustrates annotated sentences in accordance with an aspect. See FIG. 24 for annotated deconstructions of the pseudocode, 1-1 through 1-3.
  • FIG. 25 illustrates annotated sentences in accordance with an aspect. See FIG. 25 for annotated deconstructions of the pseudocode, 1-5 through 2-3. [0314] Finally, we have
  • De Boni proposed a method of determining the appropriateness of an answer to a question through a proof of logical relevance rather than a logical proof of truth. See De Boni, Marco, Using logical relevance for question answering, Journal of Applied Logic, Volume 5, Issue 1, March 2007, Pages 92-103.
  • logical relevance as the idea that answers should not be considered as absolutely true or false in relation to a question, but should be considered true more flexibly in a sliding scale of aptness. Then it becomes possible to reason rigorously about the appropriateness of an answer even in cases where the sources of answers are incomplete or inconsistent or contain errors.
  • the authors show how logical relevance can be implemented through the use of measured simplification, a form of constraint relaxation, in order to seek a logical proof than an answer is in fact an answer to a particular question.
  • Adjacency pairs are defined as pairs of utterances that are adjacent, produced by different speakers, ordered as first part and second part, and typed — a particular type of first part requires a particular type of second part.
  • Adjacency pairs are relational by nature, but they could be reduced to labels (‘first part’, ‘second part’, ‘none’), possibly augmented with a pointer towards the other member of the pair.
  • Frequently encountered observed kinds of adjacency pairs include the following ones: request / offer / invite accept / refuse; assess agree / disagree; blame denial / admission; question answer; apology downplay; thank welcome; greeting greeting. See Levinson, Stephen C. 2000. Presumptive Meanings: The Theory of Generalized Conversational Implicature. Cambridge, MA: The MIT Press.
  • Rhetoric relations similarly to adjacency pairs, are a relational concept, concerning relations between utterances, not utterances in isolation. It is however possible, given that an utterance is a satellite with respect to a nucleus in only one relation, to assign to the utterance the label of the relation. This poses strong demand for a deep analysis of dialogue structure.
  • the number of rhetoric relations in RST ranges from the ‘dominates’ and ‘satisfaction- precedes’ classes used by (Grosz and Sidner 1986) to more than a hundred types.
  • Coherence relations are an alternative way to express rhetoric structure in text. See Scholman, Merel , Jacqueline Evers-Vermeul, Ted Sanders. Categories of coherence relations in discourse annotation. Dialogue & Discourse, Vol 7, No 2 (2016)
  • the neural network language model proposed in (engio 2003 uses the concatenation of several preceding word vectors to form the input of a neural network, and tries to predict the next word. See Bengio, Yoshua, Rejean Ducharme, Pascal Vincent, and Christian Janvin. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3 (March 2003), 1137- 1155. The outcome is that after the model is trained, the word vectors are mapped into a vector space such that Distributed Representations of Sentences and Documents semantically similar words have similar vector representations. This kind of model can potentially operate on discourse relations, but it is hard to supply as rich linguistic information as we do for tree kernel learning.
  • Reichman 1985 gives a formal description and an ATN (Augmented Transition Network) model of conversational moves, with reference to conventional methods for recognizing the speech act of an utterance.
  • the author uses the analysis of linguistic markers similar to what is now used for rhetoric parsing such as pre-verbal ‘please’, modal auxiliaries, prosody, reference, clue phrases (such as ‘Yes, but...’ (sub-argument concession and counter argument), ‘Yes, and...’ (argument agreement and further support), ‘No’ and ‘Yes’ (disagreement/agreement), ‘Because...’ (support), etc.) and other illocutionary indicators.
  • clue phrases such as ‘Yes, but...’ (sub-argument concession and counter argument), ‘Yes, and...’ (argument agreement and further support), ‘No’ and ‘Yes’ (disagreement/agreement), ‘Because...’ (support), etc.
  • Reichman, R. 1985 Getting computers to talk like you and me: discourse context, focus and semantics
  • Agent A recognizes Agent B’s goal to find out the answer, and it adopts a goal to tell B the answer in order to be co-operative. A then plans to achieve the goal, thereby generating the answer.
  • Agent B must adopt agent B’s goals as her own. As a result, it does not explain why A says anything when she does not know the answer or when she is not ready to accept B’s goals.
  • Mann proposed a library of discourse level actions, sometimes called dialogue games, which encode common communicative interactions. See Mann, William and Sandra Thompson. 1988. Rhetorical structure theory: Towards a functional theory of text organization. Text-Interdisciplinary Journal for the Study of Discourse, 8(3):243- 281. To be co-operative, an agent must always be participating in one of these games. So if a question is asked, only a fixed number of activities, namely those introduced by a question, are co operative responses. Games provide a better explanation of coherence, but still require the agents to recognize each other’s intentions to perform the dialogue game. As a result, this work can be viewed as a special case of the intentional view. Because of this separation, they do not have to assume co-operation on the tasks each agent is performing, but still require recognition of intention and co-operation at the conversational level. It is left unexplained what goals motivate conversational co-operation.
  • FIG. 26 illustrates discourse acts of a dialogue in accordance with an aspect.
  • Tsui (1994) characterizes the discourse acts according to a three-part transaction.
  • Her systems of choice for Initiating, Responding and follow-up are shown in FIG. 26 on the top, middle and bottom correspondingly.
  • FIG. 27 illustrates discourse acts of a dialogue in accordance with an aspect.
  • K(a) the clause logically expressing the semantics of the utterance a.
  • topic of an utterance is defined here in terms of sets of objects in the domain ontology, referred to in a determined manner in the utterance. Hence, the topic relations between utterances are computed using the task/domain ontology, handled by the task controller.
  • Rhetoric relations and argumentation [0343] Frequently, the main means of linking questions and answers is logical argumentation. There is an obvious connection between RST and argumentation relations which tried to learn in this study. There are four types of relations: the directed relations support, attack, detail, and the undirected sequence relation.
  • the support and attack relations are argumentative relations, which are known from related work. See Peldszus, A. and Stede, M. 2013. From Argument Diagrams to Argumentation Mining in Texts: A Survey. Int. J of Cognitive Informatics and Natural Intelligence 7(1), 1-31). The latter two correspond to discourse relations used in RST.
  • the argumentation sequence relation corresponds to “Sequence” in RST
  • the argumentation detail relation roughly corresponds to “Background” and “Elaboration”.
  • Argumentation detail relation is important because many cases in scientific publications, where some background information (for example the definition of a term) is important for understanding the overall argumentation.
  • a support relation between an argument component Resp and another argument component Req indicates that Resp supports (reasons, proves) Req.
  • an attack relation between Resp and Req is annotated if Resp attacks (restricts, contradicts) Req.
  • the detail relation is used, if Resp is a detail of Req and gives more information or defines something stated in Req without argumentative reasoning.
  • we link two argument components (within Req or Resp) with the sequence relation if the components belong together and only make sense in combination, i.e., they form a multi -sentence argument component.
  • Dialogue chatbot systems need to be capable of understanding and matching user communicative intentions, reason with these intentions, build their own respective communication intentions and populate these intentions with actual language to be communicated to the user.
  • Discourse trees on their own do not provide representation for these communicative intents.
  • communicative discourse trees built upon the traditional discourse trees, which can be massively produced nowadays on one hand and constitute a descriptive utterance-level model of a dialogue on the other hand.
  • Handling dialogues via machine learning of communicative discourse trees allowed us to model a wide array of dialogue types of collaboration modes and interaction types (planning, execution, and interleaved planning and execution).
  • CDTs which now include labels for communicative actions in the form of substituted VerbNet frames.
  • the positive training set was constructed from the correct pairs obtained from Yahoo Answers, social network, corporate conversations including Enron emails, customer complaints and interviews by journalists.
  • the corresponding negative training set was created by attaching responses for different, random requests and questions that included relevant keywords so that relevance similarity between requests and responses are high.
  • the evaluation showed that it is possible to recognize valid pairs in 68-79% of cases in the domains of weak request-response agreement and 80-82% of cases in the domains of strong agreement. These accuracies are essential to support automated conversations. These accuracies are comparable with the benchmark task of classification of discourse trees themselves as valid or invalid, and also with factoid question-answering systems.
  • aspects described herein use communicative discourse trees to determine whether a text contains argumentation. Such an approach can be useful, for example, for chatbots to be able to determine whether a user is arguing or not.
  • chatbots When a user attempts to provide an argument for something, a number of argumentation patterns can be employed.
  • An argument can be a key point of any communication, persuasive essay, or speech.
  • a communicative discourse tree for a given text reflects the argumentation present in the text. For example, the basic points of argumentation are reflected in the rhetoric structure of text where an argument is presented. A text without argument has different rhetoric structures. See Moens, Marie-Francine, Erik Boiy, Raquel Mochales Palau, and Chris Reed. 2007. Automatic detection of arguments in legal texts. In Proceedings of the 11th International Conference on Artificial Intelligence and Law, ICAIL ⁇ 7, pages 225-230, Stanford, CA, USA.jAdditionally, argumentation can differ between domains. For example, for product recommendation, texts with positive sentiments are used to encourage a potential buyer to make a purchase. In the political domain, the logical structure of sentiment versus argument versus agency is much more complex.
  • Machine learning can be used in conjunction with communicative discourse trees to determine argumentation. Determining argumentation can be tackled as a binary classification task in which a communicative discourse tree that represents a particular block of text is provided to a classification model. The classification model returns a prediction of whether the communicative discourse tree is in a positive class or a negative class. The positive class corresponds to texts with arguments and the negative class corresponds to texts without arguments. Aspects described herein can perform classification based on different syntactic and discourse features associated with logical argumentation. In an example, for a text to be classified as one containing an argument, the text is similar to the elements of the first class to be assigned to this class. To evaluate the contribution of our sources, two types of learning can be used: nearest neighbor and statistical learning approaches.
  • Nearest Neighbor (kNN) learning uses explicit engineering of graph descriptions.
  • the similarity measured is the overlap between the graph of a given text and that of a given element of training set.
  • the machine learning approaches estimate the contribution of each feature type and the above learning methods to the problem of argument identification including the presence of opposing arguments (Stab and Gurevych, 2016). More specifically, aspects use the rhetoric relations and how the discourse and semantic relations work together in an argumentation detection task.
  • sentiment analysis is necessary for a broad range of industrial applications, its accuracy remains fairly low. Recognition of a presence of an argument, if done reliably, can potentially substitute some opinion mining tasks when one intends to differentiate a strong opinionated content from the neutral one. Argument recognition result can then serve as a feature of sentiment analysis classifier, differentiating cases with high sentiment polarity from the neutral ones, ones with low polarity.
  • a chatbot can use the information in Table 4 to personalize responses or tailor search results or opinionated data to user expectations. For example, a chatbot can consider political viewpoint when providing news to a user. Additionally, personalizing responses is useful for product recommendations. For example, a particular user might prefer skis over snowboards as evidenced by a user’s sharing of stories of people who do not like snowboarders. In this manner, the aspects described herein enable a chatbot can behave like a companion, by showing empathy and ensuring that the user does not feel irritated by the lack of common ground with the chatbot.
  • a RST representation of the arguments is constructed and aspects can observe if a discourse tree is capable of indicating whether a paragraph communicates both a claim and an argumentation that backs it up. Additional information is added to a discourse tree such that it is possible to judge if it expresses an argumentation pattern or not. According to the Wall Street Journal, this is what happened : “Since October [2015], the Wall Street Journal has published a series of anonymously sourced accusations that inaccurately portray Theranos. Now, in its latest story (“U.S. Probes Theranos Complaints,” Dec.
  • FIG. 28 depicts an exemplary communicative discourse tree in accordance with an aspect.
  • FIG. 28 depicts discourse tree 2800, communicative action 2801 and communicative action 2802. More specifically, discourse tree 2800 represents the following paragraph: “But Theranos has struggled behind the scenes to turn the excitement over its technology into reality. At the end of 2014, the lab instrument developed as the linchpin of its strategy handled just a small fraction of the tests then sold to consumers, according to four former employees.” As can be seen, when arbitrary communicative actions are attached to the discourse tree 2800 as labels of terminal arcs, it becomes clear that the author is trying to bring her point across and not merely sharing a fact. As shown, communicative action 2801 is a “spitgle” and communicative action 2802 is “develop.”
  • FIG. 29 depicts an exemplary communicative discourse tree in accordance with an aspect.
  • FIG. 29 depicts discourse tree 2900, which represents the following text: “Theranos remains actively engaged with its regulators, including CMS and the FDA, and no one, including the Wall Street Journal, has provided Theranos a copy of the alleged complaints to those agencies. Because Theranos has not seen these alleged complaints, it has no basis on which to evaluate the purported complaints.” But as can be seen, from only the discourse tree and multiple rhetoric relations of elaboration and a single instance of background, it is unclear whether an author argues with his opponents or enumerates some observations. Relying on communicative actions such as “engaged” or “not see”, CDT can express the fact that the author is actually arguing with his opponents
  • FIG. 30 depicts an exemplary communicative discourse tree in accordance with an aspect.
  • FIG. 30 depicts discourse tree 3000, which represents the following text, in which Theranos is attempting to get itself off the hook: “It is not unusual for disgruntled and terminated employees in the heavily regulated health care industry to file complaints in an effort to retaliate against employers for termination of employment. Regulatory agencies have a process for evaluating complaints, many of which are not substantiated. Theranos trusts its regulators to properly investigate any complaints.”
  • FIG. 31 depicts an exemplary communicative discourse tree in accordance with an aspect.
  • FIG. 31 depicts discourse tree 3100, which represents the following text for Theranos’ argument that the opponent’s arguments are faulty: “By continually relying on mostly anonymous sources, while dismissing concrete facts, documents, and expert scientists and engineers in the field provided by Theranos, the Journal denies its readers the ability to scrutinize and weigh the sources’ identities, motives, and the veracity of their statements.”
  • Theranos’ argument is weak is because the company tries to refute the opponent’s allegation concerning the complaints about Theranos’ s services from clients. Theranos’ demand for evidence by inviting WSJ to disclose the sources and the nature of the complaints is weak. A claim is that a third-party (independent investigative agent) would be more reasonable and conclusive. However, some readers might believe that the company’s argument (burden of proof evasion) is logical and valid. Note that an argumentation assessor cannot identify the rhetorical relations in a text by relying on text only. Rather, the context of the situation is helpful in order to grasp the arguer’s intention.
  • FIG. 32 depicts an example communicative discourse tree in accordance with an aspect.
  • FIG. 32 depicts communicative discourse tree 3200 for this second example.
  • an acceptable proof would be to share a certain observation, associated from the standpoint of peers, with the absence of a chemical attack. For example, if it is possible to demonstrate that the time of the alleged chemical attack coincided with the time of a very strong rain, that would be a convincing way to attack this claim. However, since no such observation was identified, the source, Russia Today, resorted to plotting a complex mental states concerning how the claim was communicated, where it is hard to verify most statements about the mental states of involved parties.
  • FIG. 33 depicts an example communicative discourse tree in accordance with an aspect.
  • FIG. 33 depicts communicative discourse tree 3300 for another controversial story, a Trump- Russian link acquisition (BBC 2018).
  • BBC 2018 Trump-Russia link acquisition
  • the BBC was unable to confirm the claim, so the story is repeated and over and over again to maintain a reader expectation that it would be instantiated one day.
  • the goal of the author is to make the audience believe that such dossier exists without misrepresenting events.
  • the author can attach a number of hypothetical statements about the existing dossier to a variety of mental states to impress the reader in the authenticity and validity of the topic.
  • FIG. 34 depicts an example communicative discourse tree in accordance with an aspect.
  • FIG. 34 depicts communicative discourse tree 3400 for an example of a heated argumentation.
  • the following text, represented by communicative discourse tree 3400 illustrates an example of a CDT for a heated argumentation of a customer treated badly by a credit card company American Express (Amex) in 2007.
  • the communicative discourse tree 3400 shows a sentiment profile.
  • a sentiment profile is a sentiment value attached to an indication of a proponent (in this case, “me”) and an opponent (in this case, “Amex”).
  • the proponent is almost always positive and the opponent is negative confirms the argumentation flow of this complaint. Oscillating sentiment values would indicate that there is an issue with how an author provides argumentation.
  • FIG. 35 depicts an example communicative discourse tree in accordance with an aspect.
  • FIG. 35 depicts communicative discourse tree 3500 that represents a text advising on how to behave communicating an argument: “When a person is in the middle of an argument, it can be easy to get caught up in the heat of the moment and say something that makes the situation even worse. None can make someone more frenzied and hysterical than telling them to calm down. It causes the other person to feel as if one is putting the blame for the elevation of the situation on them. Rather than actually helping them calm down, it comes off as patronizing and will most likely make them even angrier.”
  • Fig. 35 is an example of meta argumentation.
  • a meta-argumentation is an argumentation on how to conduct heated argumentation, which can be expressed by the same rhetorical relations.
  • FIG. 36 depicts an exemplary process for using machine learning to determine argumentation in accordance with an aspect.
  • process 3600 involves accessing text comprising fragments.
  • Application 102 can text from different sources such input text 130, or Internet-based sources such as chat, Twitter, etc. Text can consist of fragments, sentences, paragraphs, or longer amounts.
  • process 3600 involves creating a discourse tree from the text, the discourse tree including nodes and each nonterminal node representing a rhetorical relationship between two of the fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments.
  • Application 102 creates discourse in a substantially similar manner as described in block 1502 in process 1500.
  • process 3600 involves matching each fragment that has a verb to a verb signature, thereby creating a communicative discourse tree.
  • Application 102 creates discourse in a substantially similar manner as described in steps 1503-1505 in process 1500.
  • process 3600 involves determining whether the communicative discourse tree includes argumentation by applying a classification model trained to detect argumentation to the communicative discourse tree.
  • the classification model can use different learning approaches. For example, the classification model can use a support vector machine with tree kernel learning. Additionally, the classification model can use nearest neighbor learning of maximal common sub-trees.
  • application 102 can use machine learning to determine similarities between the communicative discourse tree identified at block 3603 and one or more communicative discourse trees from a training set of communicative discourse trees.
  • Application 102 can select an additional communicative discourse tree from a training set that includes multiple communicative discourse trees. Training can be based on the communicative discourse tree having a highest number of similarities with the additional communicative discourse tree.
  • Application 102 identifies whether the additional communicative discourse tree is from a positive set or a negative set. The positive set is associated with text containing argumentation and the negative set is associated with text containing no argumentation. Application 102 determines based on this identification whether the text contains an argumentation or no argumentation.
  • Both positive and negative datasets include 8800 texts.
  • An average text size was 400 words (always above 200 and below 1000 words).
  • Amazon Mechanical Turk to confirm that the positive dataset includes argumentation in a commonsense view, according to the employed workers. Twelve workers who had the previous acceptance score of above 85% were assigned the task to label.
  • For manual confirmation of the presence and absence of arguments we randomly selected representative from each set (about 10%) and made sure they properly belong to a class with above 95% confidence. We avoided sources where such confidence was below 95%.
  • For first portion of texts which were subject to manual labeling we conducted an assessment of inter-annotator agreement and observed that it exceeded 90%. Therefore for the rest of annotations we relied on a single worker per text.
  • For the evaluation we split out dataset into the training and test part in proportion of 4: 1.
  • This dataset includes more emotionally-heated complaints in comparison with other argument mining datasets. For a given topic such as insufficient funds fee, this dataset provides many distinct ways of argumentation that this fee is unfair. Therefore, our dataset allows for systematic exploration of the topic-independent clusters of argumentation patterns and observe a link between argumentation type and overall complaint validity.
  • Other argumentation datasets including legal arguments, student essays (Stab and Gurevych 2017), internet argument corpus (Abbot et al., 2016), fact-feeling dataset (Oraby et ah, 2016) and political debates have a strong variation of topics so that it is harder to track a spectrum of possible argumentation patterns per topic.
  • a naive approach is just relying on keywords to figure out a presence of argumentation. Usually, a couple of communicative actions so that at least one has a negative sentiment polarity (related to an opponent) are sufficient to deduce that logical argumentation is present. This naive approach is outperformed by the top performing CDT approach by 29%. A Naive Bayes classifier delivers just 2% improvement.
  • Table 7 shows the SVM TK argument detection results per source. As a positive set, we now take individual source only. The negative set is formed from the same sources but reduced in size to match the size of a smaller positive set. The cross-validation settings are analogous to our assessment of the whole positive set.
  • Pattern - specific argumentation detection results are shown in Table 8.
  • the first and second type of argument is harder to recognize (by 7-10% below the general argument) and the third and fourth type is easier to detect (exceeds the general argument accuracy by 3%).
  • CDT significantly deviated CDTs.
  • a CDT for the purpose of this assessment we considered a CDT to be deviated if more than 20% of rhetoric relations is determined improperly.
  • the distortion evaluation dataset is significantly smaller than the detection dataset since substantial manual efforts is required and the task cannot be submitted to Amazon Mechanical Turk workers.
  • FIG. 37 is a fragment of a discourse tree in accordance with an aspect.
  • FIG. 37 depicts discourse tree 3700, which represents the following text.
  • FIG 38 depicts a discourse tree for a borderline review in accordance with an aspect.
  • FIG. 38 depicts discourse tree 3800 for a borderline review.
  • a borderline review is negative from the discourse point of view and neutral from the reader’s standpoint.
  • FIG. 39 depicts a discourse tree for a sentence showing compositional semantic approach to sentiment analysis in accordance with an aspect.
  • FIG. 39 depicts discourse tree 3900.
  • Sentiment analysis is calculated based on global polarity, not dependent on individual elements of the sentence, but more interestingly, on the discourse level structure (macro-structure). For example, “high reliability” is neutral in “I want a car with high reliability” because though it is a positive property, it does not refer to any specific car.
  • aspects of the present disclosure validate argumentation.
  • Application 102 extracts an argumentation structure from a body of text and represents the argumentation via a communicative discourse tree (CDT). Subsequently, application 102 can verify that the claim, or target claim, in the text is valid, i.e., is not logically attacked by other claims, and is consistent with external truths, i.e., rules. With domain knowledge, the validity of a claim can be validated. However, in some cases, domain knowledge may be unavailable and other domain-independent information, such as writing style and writing logic, are used.
  • CRM Customer Relationship Management
  • argument-mining is a linguistic-based, and logical validation of an argument, which is logic based.
  • the concept of automatically identifying argumentation schemes was first discussed in (Walton et ah, 2008). Ghosh et al. (2014) investigates argumentation discourse structure of a specific type of communication - online interaction threads. Identifying argumentation in text is connected to the problem of identifying truth, misinformation and disinformation on the web (Pendyala and Figueira, 2015, Galitsky 2015, Pisarevskaya et al 2015). In (Lawrence and Reed, 2015) three types of argument structure identification are combined: linguistic features, topic changes and machine learning. As explained further herein, some aspects employ Defeasible Logic Programming (DeLP) (Garcia and Simari, 2004; Alsinet et al., 2008) in conjunction with communicative discourse trees.
  • DeLP Defeasible Logic Programming
  • FIG. 40 depicts an exemplary process 4000 for validating arguments in accordance with an aspect.
  • Application 102 can perform process 4000.
  • process 4000 involves accessing text that includes fragments.
  • process 4000 performs substantially similar steps as described in block 3601 of process 3600.
  • Text can include input text 130, which can be a paragraph, sentence, utterance, or other text.
  • process 4000 involves identifying a presence of argumentation in a subset of the text by creating a communicative discourse tree from the text and applying a classification model trained to detect argumentation to the communicative discourse tree.
  • process 4000 performs substantially similar steps as described in blocks 3602- 3604 of process 3600. Other methods of argumentation detection can be used.
  • a block 4003, process 4000 involves evaluating the argumentation by using a logic system.
  • Application 102 can use different types of logic systems to evaluate the argumentation.
  • DeLP Defeasible Logic Programming
  • FIG. 42 depicts exemplary operations that can implement block 4003. For illustrative purposes, process 4000 is discussed with respect to FIG. 41.
  • FIG. 41 depicts an exemplary communicative discourse tree for an argument in accordance with an aspect.
  • FIG. 41 includes communicative discourse tree 4101.
  • Communicative discourse tree 4101 includes node 4120 and other nodes, some of which are labeled with communicative actions 4110-4117.
  • CDT 4101 represents the following text: “The landlord contacted me, the tenant, and the rent was requested. However, I refused the rent since I demanded repair to be done. I reminded the landlord about necessary repairs, but the landlord issued the three-day notice confirming that the rent was overdue. Regretfully, the property still stayed unrepaired.”
  • FIG. 42 depicts an exemplary method for validating arguments using defeasible logic programming in accordance with an aspect.
  • Defeasible logic programming (DeLP) is a set of facts, strict rules P of the form (A:-B), and a set of defeasible rules D of the form A- ⁇ B, whose intended meaning is “if B is the case, then usually A is also the case.”
  • the communicative discourse tree indicates valuable information, such as how the facts are inter-connected by defeasible rules.
  • Elementary discourse units of the CDT that are of rhetorical relation type “contrast” and communicative actions that are of type “disagree” indicate defeasible rules.
  • method 4200 involves creating a fixed part of a logic system.
  • the fixed part of the logic system includes one or more claim terms and one or more domain definition clauses.
  • Domain definition clauses are associated with a domain of the text and can include legal, scientific terms, and commonsense knowledge in a particular domain.
  • a scientific example is “if a physical body is moving with acceleration, it is subject to a physical force.”
  • an example of a standard definition is: “if repair is done -> home is habitable and appliances are working.”
  • the text contains a target claim to be evaluated “rent_receipt,” i.e. “was the rent received?”
  • Application 102 also extracts the following clause “repair is done - ⁇ rent refused” from the text “refused the rent since I demanded repair to be done.”
  • method 4200 involves creating a variable part of the logic system by determining a set of defeasible rules and a set of facts.
  • Application 102 determines, from the communicative discourse tree, a set of defeasible rules by extracting, from the communicative discourse tree, one or more of (i) an elementary discourse unit that is a rhetorical relation type contrast and (ii) a communicative action that is of a class type disagree.
  • the class disagree includes actions such as “deny,” “have different opinion, “not believe,” “refuse to believe,” “contradict,” “diverge,” “deviate,” “counter,” “differ,” “dissent,” “be dissimilar.” Other examples are possible.
  • Application 102 determines the following defeasible rules: rent receipt - ⁇ rent deposit transaction, rent deposit transaction - ⁇ contact tenant. rent deposit transaction - ⁇ contact_tenant, three days notice is issued. rent deposit transaction - ⁇ rent is overdue. repair is done - ⁇ rent refused, repair is done. repair is done - ⁇ rent is requested. rent deposit transaction - ⁇ tenant short on money, repair is done. repair is done - ⁇ repair is requested. h repair is done - ⁇ rent_is_requested. h repair is requested - ⁇ stay unrepaired h repair is done - ⁇ stay unrepaired.
  • application 102 determines additional facts from communicative actions that are of type “disagree.” Continuing the example, and referring back to FIG. 41, application 102 determines the following facts from the subjects of the communicative actions of the CDT: contact tenant (communicative action 4111), rent is requested (communicative action 4112), rent refused (communicative action 4113), stay unrepaired (communicative action 4114), remind about repair (communicative action 4115), three days notice is issued (communicative action 4116), and rent is overdue (communicative action 4117).
  • method 4200 involves determining a defeasible derivation comprising a set of non-contradictory defeasible rules from the defeasible set of rules.
  • h be a literal
  • R (P, D) a DeLP program.
  • ⁇ A, h> is an argument for h, if A is a set of defeasible rules of D, such that:
  • A is minimal: there is no proper subset Ao of A such that Ao satisfies conditions (1) and (2).
  • ⁇ A, h> is a minimal non-contradictory set of defeasible rules, obtained from a defeasible derivation for a given literal h associated with a program P.
  • a minimal subset means that no subset exists that satisfies conditions 1 and 2
  • method 4200 involves creating one or more defeater arguments from the set of facts.
  • Defeaters are arguments which can be in their turn attacked by other arguments, as is the case in a human dialogue.
  • An argumentation line is a sequence of arguments where each element in a sequence defeats its predecessor.
  • Defeater arguments can be formed in the following manner. For example, argument ⁇ Ai, hi> attacks ⁇ Ai, hi> iff (if and only if) there exists a sub-argument ⁇ A, h> of ⁇ Ai, hi> (Ac Ai) such that h and hi are inconsistent (i.e. P ⁇ (h, hi ⁇ derives complementary literals).
  • ⁇ Ai, hi> defeats ⁇ Ai , h2> if ⁇ Ai, hi> attacks ⁇ A2, h2> at a sub-argument ⁇ A, h> and ⁇ Ai, hi> is strictly preferred (or not comparable to) ⁇ A, h>.
  • ⁇ Ai, hi> as a proper defeater
  • method 4200 involves constructing, from the defeasible derivation, a dialectic tree including a root node representing the argument and leaf nodes that represent the defeater arguments.
  • Target claims can be considered DeLP queries which are solved in terms of dialectical trees, which subsumes all possible argumentation lines for a given query.
  • the definition of dialectical tree provides us with an algorithmic view for discovering implicit self-attack relations in users’ claims. Let ⁇ Ao, ho> be an argument (target claim) from a program P. For discussion purposes, block 4205 is discussed with respect to FIG. 43.
  • FIG. 43 depicts an exemplary dialectic tree in accordance with an aspect.
  • FIG. 43 depicts the dialectical tree for the text developed above.
  • FIG. 43 includes dialectical tree 4300, which includes root node 4301 and nodes 4302-4307.
  • Dialectical tree 4300 is based on ⁇ Ao, ho>, which is defined as follows:
  • root node 4301 The root of the tree (root node 4301) is labeled with ⁇ Ao, ho>
  • [ ⁇ Bo, qo>, ⁇ Bi, qi>, ..., ⁇ Bk, qk>] all attack ⁇ A n , h n >.
  • a labeling on the dialectical tree can be then performed as follows:
  • Any inner node is to be labeled as a U-node whenever all of its associated children nodes are labeled as D-nodes. 3. Any inner node is to be labeled as a D-node whenever at least one of its associated children nodes is labeled as U-node.
  • method 4200 involves evaluating the dialectic tree by recursively evaluating the defeater arguments.
  • (1) and (2) are proper defeaters and the last one is a blocking defeater. Observe that the first argument structure has the counter-argument, ⁇ rent deposit transaction - ⁇ tenant short on money ⁇ , rent deposit transaction) , but it is not a defeater because the former is more specific. Thus, no defeaters exist and the argumentation line ends there.
  • Bs above has a blocking defeater ⁇ rent deposit transaction - ⁇ tenant short on money) ⁇ , rent deposit transaction ⁇ , which is a disagreement sub-argument of ⁇ A, rent receipt> and it cannot be introduced since it gives rise to an unacceptable argumentation line.
  • Ci has a blocking defeater that can be introduced in the line ⁇ Di, repair is done > , where I)i ⁇ ( repair is done - ⁇ stay unrepaired) ⁇ .
  • Di and C2 have a blocking defeater, but they cannot be introduced because they make the argumentation line inacceptable. Hence the state rent receipt cannot be reached, as the argument supporting the literal rent receipt, is not warranted.
  • method 4200 involves responsive to determining that none of the defeater arguments are contradictory with the defeasible derivation, identifying the claim supported by the argument as valid. A determination that no contradictory arguments exits indicates that the claim is valid, whereas a determination that contradictory arguments exists indicates that the claim is invalid. Rhetoric classification 102 can then perform an action based on the validation, such as providing different answers to a user device based on the validity of the claim.
  • a typical case abstract is like the following: “Tenants complained of a reduction in building-wide services. They said that the building super didn’t make needed repairs as requested and that landlord had refused to perform repairs in their apartment. They also complained about building accessibility issues. Among other things, the building side door walkway was reconstructed and made narrower. This made it hard to navigate a wheelchair through that doorway. The DRA ruled against tenants, who appealed and lost.”
  • a baseline argument detection approach relies on keywords and syntactic features to detect argumentation (Table 13.8). Frequently, a coordinated pair of communicative actions (so that at least one has a negative sentiment polarity related to an opponent) is a hint that logical argumentation is present. This naive approach is outperformed by the top performing TK learning CDT approach by 29%. SVM TK of CDT outperforms SVM TK for RST+CA and RST + full parse trees (Galitsky, 2017) by about 5% due to noisy syntactic data which is frequently redundant for argumentation detection.
  • SVM TK approach provides acceptable F-measure but does not help to explain how exactly the affective argument identification problem is solved, providing only final scoring and class labels. Nearest neighbor maximal common sub-graph algorithm is much more fruitful in this respect (Galitsky et al., 2015). Comparing the bottom two rows, we observe that it is possible, but infrequent to express an affective argument without CAs. [0459] Assessing logical arguments extracted from text, we were interested in cases where an author provides invalid, inconsistent, self-contradicting cases. That is important for chatbot as a front end of a CRM systems focused on customer retention and facilitating communication with a customer (Galitsky et ah, 2009). The domain of residential real estate complaints was selected and a DeLP thesaurus was built for this domain. Automated complaint processing system can be essential, for example, for property management companies in their decision support procedures (Constantinos et ah, 2003).
  • Performing a syntactic generalization of two sentences involves identifying a word in each of the sentences and/or an identical part of speech (POS) in each of the sentences.
  • POS part of speech
  • a lemma refers to a word without the related part-of-speech information. If the lemmas for two words are different but the part of speech for each word is the same, then the part of speech is part of the generalized result. If the lemmas are the same but the parts of speech are different, the lemma part of the generalized result.
  • zoom(type of zoom) [0469] zoom(type of zoom).
  • subject l subject2 ⁇ *,POS(subjectl), word2vecDistance(subjectl subject2)>. If part-of-speech is different, generalization is an empty tuple. It cannot be further generalized.
  • generalization includes all maximum ordered sets of generalization nodes for words in phrases so that the order of words is retained.
  • the generalization is ⁇ ⁇ JJ-digital , NN-camera> , ⁇ NN- today, ADV, Monday J , where the generalization for noun phrases is followed by the generalization for an adverbial phrase.
  • Verb buy is excluded from both generalizations because it occurs in a different order in the above phrases.
  • Buy - digital - camera is not a generalization phrase because buy occurs in different sequence with the other generalization nodes.
  • Certain aspects relate to improved discourse parsers. Prediction of a rhetorical relation between two sentences is the goal of discourse parsing, along with text segmentation (splitting sentences into elementary discourse units). While documents can be analyzed as sequences of hierarchical discourse structures, an issue in discourse coherence is how rhetorical relations are signaled by the source text (and can be identified by a parser). For example, rhetorical relations are often signaled by discourse markers such as and, because, however and while , and relations are sometimes classified as explicit relations if they contain such markers. Discourse markers are reliable signals of coherence relations.
  • FIG. 44 depicts a discourse tree and a semantic tree, in accordance with an aspect.
  • FIG. 44 depicts discourse tree 4400 and semantic tree 4410.
  • Discourse tree 4400 and semantic tree 4410 each represent the following text: “It was a question of life or death for me : I had scarcely enough drinking water to last a week. ”
  • Discourse tree 4410 is represented in text-based form as follows (indentation refers to level of nesting in the tree): elaboration
  • TEXT I had scarcely enough drinking water TEXT :to last a week.
  • semantic relation 4422 has a semantic role is related to the verb drink , identified as role 4424.
  • a nucleus EDU (“I had scarcely enough drinking water”) that has drink in discourse tree 4400 can be identified because discourse tree 4410 and semantic tree 4420 have a common entity — “drink.”
  • a mapping between AMR semantic relations and rhetorical relations is developed as a result of manual generalization of available AMR annotations and is shown below in Table 14 below.
  • Table 14 illustrates examples of semantic roles and corresponding rhetorical relations.
  • the first column enumerates the rhetorical relation to be detected.
  • the second column represents the AMR semantic relations being mapped into the rhetorical ones.
  • the third column provides the example sentence that is going to be matched again a sentence being rhetorically parsed.
  • the fourth column shows the AMR parsing for the templates.
  • Table 15 below provides examples of a refined discourse tree where Elaboration is turned into a specific relation.
  • a template is built and refined.
  • the template shows the detected rhetorical relation manner in bold.
  • the second example shows actual refinement where the Elaboration is turned into Concession by applying the template from the second row from the bottom. Syntactic generalization between this template and the sentence is also shown.
  • Table 16 shows the co-occurrence values and percentages for lexical, syntactic and semantic correlation with rhetorical relation. This data helps to improve the scoring for “and” and “as” (usually ignored for syntactic generalization) and but, while, however, because usually has a very low score.
  • FIG. 45 depicts a discourse tree and a semantic tree, in accordance with an aspect.
  • FIG. 45 depicts discourse tree 4510 and semantic tree 4520.
  • Discourse tree 4510 represents the text “I ate the most wonderful hamburger that she had ever bought for me.”
  • Semantic tree 4520 does not represent the same text as discourse tree 4510. Rather, semantic tree 4520 represents text of a template that is a suitable match with the text of discourse tree 4510 and can be used to improve discourse tree 4510.
  • Discourse tree 4510 is represented in text form as: elaboration
  • TEXT I ate the most wonderful hamburger TEXT : that she had ever bought for me.
  • discourse tree 4510 As can be seen from discourse tree 4510, the two elementary discourse units “I ate the most wonderful hamburger” and “that she had ever bought for me. “ are connected by rhetorical relation “elaboration.” Accordingly, discourse tree 4510 is a good candidate for improvement as the “elaboration” may not be the most accurate rhetorical relation.
  • the AMR semantic role of compared-to is mapped to rhetorical relation of Comparison.
  • the default discourse parsing provides the Elaboration that can be turned into a more accurate rhetorical relation, if the EDU pair with a default rhetorical relation is semantically similar with a template that has a specific semantic relation that can be mapped into a rhetorical relation.
  • a match against a template found in a set of semantic templates e.g., AMR repository
  • the matched template is for the sentence “7/ was the most beautiful and stately planet that he had ever seen. ”
  • TEXT I ate the most wonderful hamburger TEXT : that she had ever bought for me.
  • FIG. 46 is a flowchart of an exemplary process 4600 for generating improved discourse trees, in accordance with an aspect. It will be appreciated that in some cases, one or more operations in process 4600 may not be performed. Process 4600 can be performed by application 102.
  • process 4600 involves creating a discourse tree from text by identifying elementary discourse units in the text.
  • process 4600 involves substantially similar operations as block 1502 of process 1500.
  • the determined discourse tree includes nodes. Each nonterminal node of the nodes represents a rhetorical relationship between two elementary discourse units and each terminal node of the nodes of the discourse tree is associated with an elementary discourse unit.
  • process 4600 involves identifying, in the discourse tree, a rhetorical relation of type elaboration or joint.
  • the rhetorical relation relates two elementary discourse units, e.g., a first elementary discourse unit and a second elementary discourse unit (rather than relating two other rhetorical relations or one rhetorical relation and one elementary discourse unit).
  • first elementary discourse unit and the second elementary discourse unit form a reference sentence.
  • a first EDU is “I ate the most wonderful hamburger” and a second EDU is “that she had ever bought for me” and the rhetorical relation (prior to updating) is “elaboration.”
  • process 4600 involves determining a syntactic generalization score for each candidate sentence of a set of candidate sentences.
  • each candidate sentence has a corresponding semantic relation (e.g., AMR representation).
  • a syntactic generalization score is a number of common entities between the reference sentence and the candidate sentence. Each of the common entities share a common part of speech between the candidate sentence and the reference sentence. But the syntactic generalization score can be calculated differently in other aspects, as described below.
  • the purpose of an abstract generalization is to find commonality between portions of text at various semantic levels. Generalization can be performed on the paragraph, sentence, EDU, phrase, and individual word levels. With the exception of at the word level, the result of generalization of two expressions is a set of expressions. In such the set, for each pair of expressions so that one is less general than another, the latter is eliminated. Generalization of two sets of expressions is a set of sets of expressions which are the results of pair-wise generalization of these expressions. For example purposes, FIG. 46 is discussed with respect to FIG. 47, which illustrates generalization, and FIG. 48, which illustrates alignment.
  • FIG. 47 depicts a generalization of a sentence and a template with known semantic relation, in accordance with an aspect.
  • FIG. 47 shows the generalization of sentence 4710, which is “If you read a book at night, your knowledge will improve ” and the template 4720, which is “ If one gets lost in the night, such knowledge is valuable ” from Table 14.
  • the resulting generalization 4730 is as follows:
  • W ⁇ and, as, but, white, however, becau e ⁇ is calculated as a default value of 1 normalized for the value in the second column of Table 16. Notice that default syntactic generalization mostly ignores discourse cue words.
  • a generalization score between the reference sentence ⁇ ref sentence) and the candidate template ⁇ Template) then can be expressed as sum through phrases of the weighted sum through words ⁇ WOrdref sentence and wordtemplate.
  • score(ref sentence Jemplate) ⁇ ⁇ NP, VP, ... ⁇ ⁇ WPOS word generalization ⁇ ordrej sentence,
  • the maximal generalization can then be defined as the one with the highest score.
  • phrase level generalization starts with finding an alignment between two phrases (a correspondence of as many words as possible between two phrases).
  • the alignment operation is performed such that phrase integrity is retained. For instance, two phrases can be aligned only if the correspondence between their head nouns is established. There is a similar integrity constraint for aligning verb, prepositional and other types of phrases.
  • FIG. 48 depicts alignment between two sentences, in accordance with an aspect.
  • FIG. 48 depicts an alignment between sentence 4810, which reads “use the screw driver from this tool for fixing heaters,” and sentence 4820, which reads “get short screw driver holder for electric heaters.”
  • the resulting alignment 4830 is as follows:
  • improved discourse trees can be generated using separate generalization of the nucleus and the satellite. For instance a discourse tree is created as in block 4602 of process 4600. From the discourse tree, a rhetorical relation is identified. Suitable rhetorical relations are identified. Examples of suitable rhetorical relations include innermost relations of Elaboration and Joint , and also the nested ones ⁇ Elaboration over another Elaboration [over another Elaboration ⁇ . [0518] The nucleus EDU and the satellite EDUs are identified. If they are too complicated or long, these EDUs can be reduced in size and/or complexity. The nucleus EDU is generalized with each template (e.g., Tables 14 and/or 15).
  • the candidate sentence with a highest generalization score is selected. If the score is above a threshold, then the satellite EDU corresponding to the rhetorical relation is generalized with the template. If the generalization score of the satellite EDU is above a threshold, then the rhetorical relation is used to replace the rhetorical relation in the reference sentence. Examples of generalization thresholds are 2.0 (for the nucleus) and 3.3 (for the satellite).
  • process 4600 involves selecting the candidate sentence having a highest syntactic generalization score of the syntactic generalization scores.
  • a match is not found.
  • application 102 searches the abstract meaning representation (AMR) dataset (e.g., tables 14 and/or 15) to identify that the identified semantic relation is not in the AMR dataset and then substitutes, in the discourse tree, the rhetorical relation with an additional semantic relation that is in the AMR dataset.
  • AMR abstract meaning representation
  • process 4600 involves identifying a semantic relation corresponding to the candidate sentence.
  • the semantic relation corresponds to a word in the candidate sentence and defines a role in the candidate sentence. For instance, the semantic relation in the candidate sentence is identified in table 14 and/or table 15.
  • process 4600 involves replacing in the discourse tree, the rhetorical relation with an updated rhetorical relation that corresponds to the semantic relation, thereby creating an updated discourse tree.
  • a rhetorical relation that matches the identified semantic relation in block 4610 is identified.
  • the identified rhetorical relation is inserted in the discourse tree in place of the rhetorical relation identified at block 4604.
  • the method of agreement can be represented as a phrase fi where words ⁇ A B C D) occur together with the meaning formally expressed as ⁇ w xy z>.
  • ⁇ A E F G words that occur together with the same meaning ⁇ w t u v> as in phrase fi.
  • RST Discourse Treebank (RST-DT, Carlson et al., 2001) includes news articles and is not a good source to model text structure in other genres such as fiction, scientific texts, engineering system descriptions and legal documents. Applications of discourse parsing in these domains are critical. Therefore, even if Elaboration suffices in news presentation, one needs a more specific structure to model author’s reasoning in other genres and domains such as professional texts.
  • Penn Discourse Treebank (PDTB, Prasad et al 2017) has been a major resource for training discourse parsers. Version 3.0 is the third release in the Penn Discourse Treebank project that targets annotating the Wall Street Journal section of Treebank-2 with discourse relations. Penn Discourse Treebank Version 3 contains over 53,600 tokens of annotated relations. Some pairwise annotations were normalized, new senses were included and consistency checks in the corpus were performed. Further details about the development of PDTB are available (PDBT 2019). Since PDBR only includes news genre, the performance of discourse parsers trained on it in other genres is limited. In this work, we attempted to cure these limitations by employing a source of semantic relations from which discourse relations can be deduced.
  • the PDTB project was inspired by the observation that discourse relations are grounded in an identifiable set of explicit words or phrases (discourse connectives, discourse cues) or simply in the adjacency of two sentences.
  • the PTDB has been used by many researchers in the natural language processing community and more recently, by researchers in psycholinguistics. It has also stimulated the development of similar resources in other languages and domains.
  • Discourse parsers trained on earlier models perform very poorly recognizing very specific rhetorical relations used in PDTB 3.0. Hence the improvement described in this chapter is essential for downstream applications of the discourse parsing.
  • FIG. 49 depicts a simplified diagram of a distributed system 4900 for implementing one of the aspects.
  • distributed system 4900 includes one or more client computing devices 4902, 4904, 4906, and 4908, which are configured to execute and operate a client application such as a web browser, proprietary client (e.g., Oracle Forms), or the like over one or more network(s) 4910.
  • Server 4912 may be communicatively coupled with remote client computing devices 4902, 4904, 4906, and 4908 via network 4910.
  • server 4912 may be adapted to run one or more services or software applications provided by one or more of the components of the system.
  • the services or software applications can include non-virtual and virtual environments.
  • Virtual environments can include those used for virtual events, tradeshows, simulators, classrooms, shopping exchanges, and enterprises, whether two- or three-dimensional (3D) representations, page-based logical environments, or otherwise.
  • these services may be offered as web-based or cloud services or under a Software as a Service (SaaS) model to the users of client computing devices 4902, 4904, 4906, and/or 4908.
  • SaaS Software as a Service
  • Users operating client computing devices 4902, 4904, 4906, and/or 4908 may in turn utilize one or more client applications to interact with server 4912 to utilize the services provided by these components.
  • the software components 4918, 4920 and 4922 of system 4900 are shown as being implemented on server 4012.
  • one or more of the components of distributed system 4900 and/or the services provided by these components may also be implemented by one or more of the client computing devices 4902, 4904, 4906, and/or 4908. Users operating the client computing devices may then utilize one or more client applications to use the services provided by these components.
  • These components may be implemented in hardware, firmware, software, or combinations thereof. It should be appreciated that various different system configurations are possible, which may be different from distributed system 4900.
  • the aspect shown in the figure is thus one example of a distributed system for implementing an aspect system and is not intended to be limiting.
  • Client computing devices 4902, 4904, 4906, and/or 4908 may be portable handheld devices (e.g., an iPhone®, cellular telephone, an iPad®, computing tablet, a personal digital assistant (PDA)) or wearable devices (e.g., a Google Glass® head mounted display), running software such as Microsoft Windows Mobile®, and/or a variety of mobile operating systems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, and the like, and being Internet, e-mail, short message service (SMS), Blackberry®, or other communication protocol enabled.
  • the client computing devices can be general purpose personal computers including, by way of example, personal computers and/or laptop computers running various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems.
  • the client computing devices can be workstation computers running any of a variety of commercially-available UNIX® or UNIX-like operating systems, including without limitation the variety of GNU/Linux operating systems, such as for example, Google Chrome OS.
  • client computing devices 4902, 4904, 4906, and 4908 may be any other electronic device, such as a thin-client computer, an Internet-enabled gaming system (e.g., a Microsoft Xbox gaming console with or without a Kinect® gesture input device), and/or a personal messaging device, capable of communicating over network(s) 4910.
  • exemplary distributed system 4900 is shown with four client computing devices, any number of client computing devices may be supported. Other devices, such as devices with sensors, etc., may interact with server 4912.
  • Network(s) 4910 in distributed system 4900 may be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including without limitation TCP/IP (transmission control protocol/Internet protocol), SNA (systems network architecture), IPX (Internet packet exchange), AppleTalk, and the like.
  • network(s) 4910 can be a local area network (LAN), such as one based on Ethernet, Token-Ring and/or the like.
  • LAN local area network
  • Network(s) 4910 can be a wide-area network and the Internet.
  • a virtual network including without limitation a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infra-red network, a wireless network (e.g., a network operating under any of the Institute of Electrical and Electronics (IEEE) 802.49 suite of protocols, Bluetooth®, and/or any other wireless protocol); and/or any combination of these and/or other networks.
  • VPN virtual private network
  • PSTN public switched telephone network
  • IEEE Institute of Electrical and Electronics 802.49 suite of protocols
  • Bluetooth® Bluetooth®
  • any other wireless protocol any combination of these and/or other networks.
  • Server 4912 may be composed of one or more general purpose computers, specialized server computers (including, by way of example, PC (personal computer) servers, UNIX® servers, mid-range servers, mainframe computers, rack-mounted servers, etc.), server farms, server clusters, or any other appropriate arrangement and/or combination.
  • Server 4912 can include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization.
  • One or more flexible pools of logical storage devices can be virtualized to maintain virtual storage devices for the server.
  • Virtual networks can be controlled by server 4912 using software defined networking.
  • server 4912 may be adapted to run one or more services or software applications described in the foregoing disclosure.
  • server 4912 may correspond to a server for performing processing described above according to an aspect of the present disclosure.
  • Server 4912 may run an operating system including any of those discussed above, as well as any commercially available server operating system. Server 4912 may also run any of a variety of additional server applications and/or mid-tier applications, including HTTP (hypertext transport protocol) servers, FTP (file transfer protocol) servers, CGI (common gateway interface) servers, JAVA® servers, database servers, and the like. Exemplary database servers include without limitation those commercially available from Oracle, Microsoft, Sybase, IBM (International Business Machines), and the like.
  • server 4912 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client computing devices 4902, 4904, 4906, and 4908.
  • data feeds and/or event updates may include, but are not limited to, Twitter® feeds, Facebook® updates or real-time updates received from one or more third party information sources and continuous data streams, which may include real-time events related to sensor data applications, financial tickers, network performance measuring tools (e.g., network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like.
  • Server 4912 may also include one or more applications to display the data feeds and/or real-time events via one or more display devices of client computing devices 4902, 4904, 4906, and 4908.
  • Distributed system 4900 may also include one or more databases 4914 and 4916.
  • Databases 4914 and 4916 may reside in a variety of locations.
  • one or more of databases 4914 and 4916 may reside on a non-transitory storage medium local to (and/or resident in) server 4912.
  • databases 4914 and 4916 may be remote from server 4912 and in communication with server 4912 via a network-based or dedicated connection.
  • databases 4914 and 4916 may reside in a storage-area network (SAN).
  • SAN storage-area network
  • any necessary files for performing the functions attributed to server 4912 may be stored locally on server 4912 and/or remotely, as appropriate.
  • databases 4914 and 4916 may include relational databases, such as databases provided by Oracle, that are adapted to store, update, and retrieve data in response to SQL- formatted commands.
  • FIG. 50 is a simplified block diagram of one or more components of a system environment 5000 by which services provided by one or more components of an aspect system may be offered as cloud services in accordance with an aspect of the present disclosure.
  • system environment 5000 includes one or more client computing devices 5004, 5006, and 5008 that may be used by users to interact with a cloud infrastructure system 5002 that provides cloud services.
  • the client computing devices may be configured to operate a client application such as a web browser, a proprietary client application (e.g., Oracle Forms), or some other application, which may be used by a user of the client computing device to interact with cloud infrastructure system 5002 to use services provided by cloud infrastructure system 5002.
  • a client application such as a web browser, a proprietary client application (e.g., Oracle Forms), or some other application, which may be used by a user of the client computing device to interact with cloud infrastructure system 5002 to use services provided by cloud infrastructure system 5002.
  • cloud infrastructure system 5002 depicted in the figure may have other components than those depicted. Further, the aspect shown in the figure is only one example of a cloud infrastructure system that may incorporate an aspect of the invention. In some other aspects, cloud infrastructure system 5002 may have more or fewer components than shown in the figure, may combine two or more components, or may have a different configuration or arrangement of components.
  • Client computing devices 5004, 5006, and 5008 may be devices similar to those described above for 4902, 4904, 4906, and 4908.
  • exemplary system environment 5000 is shown with three client computing devices, any number of client computing devices may be supported. Other devices such as devices with sensors, etc. may interact with cloud infrastructure system 5002.
  • Network(s) 5010 may facilitate communications and exchange of data between client computing devices 5004, 5006, and 5008 and cloud infrastructure system 5002.
  • Each network may be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including those described above for network(s) 4910.
  • Cloud infrastructure system 5002 may comprise one or more computers and/or servers that may include those described above for server 4912.
  • services provided by the cloud infrastructure system may include a host of services that are made available to users of the cloud infrastructure system on demand, such as online data storage and backup solutions, Web-based e-mail services, hosted office suites and document collaboration services, database processing, managed technical support services, and the like. Services provided by the cloud infrastructure system can dynamically scale to meet the needs of its users.
  • a specific instantiation of a service provided by cloud infrastructure system is referred to herein as a “service instance.”
  • any service made available to a user via a communication network, such as the Internet, from a cloud service provider's system is referred to as a “cloud service.”
  • a cloud service provider's system may host an application, and a user may, via a communication network such as the Internet, on demand, order and use the application.
  • a service in a computer network cloud infrastructure may include protected computer network access to storage, a hosted database, a hosted web server, a software application, or other service provided by a cloud vendor to a user, or as otherwise known in the art.
  • a service can include password-protected access to remote storage on the cloud through the Internet.
  • a service can include a web service-based hosted relational database and a script-language middleware engine for private use by a networked developer.
  • a service can include access to an email software application hosted on a cloud vendor's web site.
  • cloud infrastructure system 5002 may include a suite of applications, middleware, and database service offerings that are delivered to a customer in a self-service, subscription-based, elastically scalable, reliable, highly available, and secure manner.
  • An example of such a cloud infrastructure system is the Oracle Public Cloud provided by the present assignee.
  • Data can be hosted and/or manipulated by the infrastructure system on many levels and at different scales.
  • Such data can include data sets that are so large and complex that it can be difficult to process using typical database management tools or traditional data processing applications. For example, terabytes of data may be difficult to store, retrieve, and process using personal computers or their rack-based counterparts.
  • Such sizes of data can be difficult to work with using most current relational database management systems and desktop statistics and visualization packages. They can require massively parallel processing software running thousands of server computers, beyond the structure of commonly used software tools, to capture, curate, manage, and process the data within a tolerable elapsed time.
  • Extremely large data sets can be stored and manipulated by analysts and researchers to visualize large amounts of data, detect trends, and/or otherwise interact with the data. Tens, hundreds, or thousands of processors linked in parallel can act upon such data in order to present it or simulate external forces on the data or what it represents.
  • These data sets can involve structured data, such as that organized in a database or otherwise according to a structured model, and/or unstructured data (e.g., emails, images, data blobs (binary large objects), web pages, complex event processing).
  • the cloud infrastructure system may be better available to carry out tasks on large data sets based on demand from a business, government agency, research organization, private individual, group of like-minded individuals or organizations, or other entity.
  • cloud infrastructure system 5002 may be adapted to automatically provision, manage and track a customer’s subscription to services offered by cloud infrastructure system 5002.
  • Cloud infrastructure system 5002 may provide the cloud services via different deployment models.
  • services may be provided under a public cloud model in which cloud infrastructure system 5002 is owned by an organization selling cloud services (e.g., owned by Oracle) and the services are made available to the general public or different industry enterprises.
  • services may be provided under a private cloud model in which cloud infrastructure system 5002 is operated solely for a single organization and may provide services for one or more entities within the organization.
  • the cloud services may also be provided under a community cloud model in which cloud infrastructure system 5002 and the services provided by cloud infrastructure system 5002 are shared by several organizations in a related community.
  • the cloud services may also be provided under a hybrid cloud model, which is a combination of two or more different models.
  • the services provided by cloud infrastructure system 5002 may include one or more services provided under Software as a Service (SaaS) category, Platform as a Service (PaaS) category, Infrastructure as a Service (IaaS) category, or other categories of services including hybrid services.
  • SaaS Software as a Service
  • PaaS Platform as a Service
  • IaaS Infrastructure as a Service
  • a customer via a subscription order, may order one or more services provided by cloud infrastructure system 5002.
  • Cloud infrastructure system 5002 then performs processing to provide the services in the customer’s subscription order.
  • the services provided by cloud infrastructure system 5002 may include, without limitation, application services, platform services and infrastructure services.
  • application services may be provided by the cloud infrastructure system via a SaaS platform.
  • the SaaS platform may be configured to provide cloud services that fall under the SaaS category.
  • the SaaS platform may provide capabilities to build and deliver a suite of on-demand applications on an integrated development and deployment platform.
  • the SaaS platform may manage and control the underlying software and infrastructure for providing the SaaS services.
  • customers can utilize applications executing on the cloud infrastructure system.
  • Customers can acquire the application services without the need for customers to purchase separate licenses and support.
  • Various different SaaS services may be provided. Examples include, without limitation, services that provide solutions for sales performance management, enterprise integration, and business flexibility for large organizations.
  • platform services may be provided by the cloud infrastructure system via a PaaS platform.
  • the PaaS platform may be configured to provide cloud services that fall under the PaaS category.
  • Examples of platform services may include without limitation services that enable organizations (such as Oracle) to consolidate existing applications on a shared, common architecture, as well as the ability to build new applications that leverage the shared services provided by the platform.
  • the PaaS platform may manage and control the underlying software and infrastructure for providing the PaaS services. Customers can acquire the PaaS services provided by the cloud infrastructure system without the need for customers to purchase separate licenses and support.
  • Examples of platform services include, without limitation, Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS), and others.
  • platform services provided by the cloud infrastructure system may include database cloud services, middleware cloud services (e.g., Oracle Fusion Middleware services), and Java cloud services.
  • database cloud services may support shared service deployment models that enable organizations to pool database resources and offer customers a Database as a Service in the form of a database cloud.
  • middleware cloud services may provide a platform for customers to develop and deploy various business applications
  • Java cloud services may provide a platform for customers to deploy Java applications, in the cloud infrastructure system.
  • infrastructure services may be provided by an IaaS platform in the cloud infrastructure system.
  • the infrastructure services facilitate the management and control of the underlying computing resources, such as storage, networks, and other fundamental computing resources for customers utilizing services provided by the SaaS platform and the PaaS platform.
  • cloud infrastructure system 5002 may also include infrastructure resources 5030 for providing the resources used to provide various services to customers of the cloud infrastructure system.
  • infrastructure resources 5030 may include pre integrated and optimized combinations of hardware, such as servers, storage, and networking resources to execute the services provided by the PaaS platform and the SaaS platform.
  • resources in cloud infrastructure system 5002 may be shared by multiple users and dynamically re-allocated per demand. Additionally, resources may be allocated to users in different time zones. For example, cloud infrastructure system 5030 may enable a first set of users in a first time zone to utilize resources of the cloud infrastructure system for a specified number of hours and then enable the re-allocation of the same resources to another set of users located in a different time zone, thereby maximizing the utilization of resources.
  • a number of internal shared services 5032 may be provided that are shared by different components or modules of cloud infrastructure system 5002 and by the services provided by cloud infrastructure system 5002.
  • These internal shared services may include, without limitation, a security and identity service, an integration service, an enterprise repository service, an enterprise manager service, a virus scanning and white list service, a high availability, backup and recovery service, service for enabling cloud support, an email service, a notification service, a file transfer service, and the like.
  • cloud infrastructure system 5002 may provide comprehensive management of cloud services (e.g., SaaS, PaaS, and IaaS services) in the cloud infrastructure system.
  • cloud management functionality may include capabilities for provisioning, managing and tracking a customer’s subscription received by cloud infrastructure system 5002, and the like.
  • cloud management functionality may be provided by one or more modules, such as an order management module 5020, an order orchestration module 5022, an order provisioning module 5024, an order management and monitoring module 5026, and an identity management module 5028.
  • modules may include or be provided using one or more computers and/or servers, which may be general purpose computers, specialized server computers, server farms, server clusters, or any other appropriate arrangement and/or combination.
  • a customer using a client device may interact with cloud infrastructure system 5002 by requesting one or more services provided by cloud infrastructure system 5002 and placing an order for a subscription for one or more services offered by cloud infrastructure system 5002.
  • the customer may access a cloud User Interface (UI), cloud UI 5012, cloud UI 5014 and/or cloud UI 5016 and place a subscription order via these UIs.
  • UI cloud User Interface
  • the order information received by cloud infrastructure system 5002 in response to the customer placing an order may include information identifying the customer and one or more services offered by the cloud infrastructure system 5002 that the customer intends to subscribe to.
  • the order information is received via the cloud UIs, 5050, 5014 and/or 5016.
  • Order database 5018 can be one of several databases operated by cloud infrastructure system 5002 and operated in conjunction with other system elements.
  • order management module 5020 may be configured to perform billing and accounting functions related to the order, such as verifying the order, and upon verification, booking the order.
  • order orchestration module 5022 may utilize the order information to orchestrate the provisioning of services and resources for the order placed by the customer. In some instances, order orchestration module 5022 may orchestrate the provisioning of resources to support the subscribed services using the services of order provisioning module 5024.
  • order orchestration module 5022 enables the management of business processes associated with each order and applies business logic to determine whether an order should proceed to provisioning.
  • order orchestration module 5022 upon receiving an order for a new subscription, order orchestration module 5022 sends a request to order provisioning module 5024 to allocate resources and configure those resources needed to fulfill the subscription order.
  • order provisioning module 5024 enables the allocation of resources for the services ordered by the customer.
  • Order provisioning module 5024 provides a level of abstraction between the cloud services provided by cloud infrastructure system 5000 and the physical implementation layer that is used to provision the resources for providing the requested services. Order orchestration module 5022 may thus be isolated from implementation details, such as whether or not services and resources are actually provisioned on the fly or pre-provisioned and only allocated/assigned upon request.
  • a notification of the provided service may be sent to customers on client computing devices 5004, 5006 and/or 5008 by order provisioning module 5024 of cloud infrastructure system 5002.
  • order management and monitoring module 5026 may be configured to collect usage statistics for the services in the subscription order, such as the amount of storage used, the amount data transferred, the number of users, and the amount of system up time and system down time.
  • cloud infrastructure system 5000 may include an identity management module 5028.
  • Identity management module 5028 may be configured to provide identity services, such as access management and authorization services in cloud infrastructure system 5000.
  • identity management module 5028 may control information about customers who wish to utilize the services provided by cloud infrastructure system 5002. Such information can include information that authenticates the identities of such customers and information that describes which actions those customers are authorized to perform relative to various system resources (e.g., files, directories, applications, communication ports, memory segments, etc.)
  • Identity management module 5028 may also include the management of descriptive information about each customer and about how and by whom that descriptive information can be accessed and modified.
  • FIG. 51 illustrates an exemplary computer system 5100, in which various aspects of the present invention may be implemented.
  • the computer system 5100 may be used to implement any of the computer systems described above.
  • computer system 5100 includes a processing unit 5104 that communicates with a number of peripheral subsystems via a bus subsystem 5102. These peripheral subsystems may include a processing acceleration unit 5106, an I/O subsystem 5108, a storage subsystem 5118 and a communications subsystem 5124.
  • Storage subsystem 5118 includes tangible computer- readable storage media 5122 and a system memory 5110.
  • Bus subsystem 5102 provides a mechanism for letting the various components and subsystems of computer system 5100 communicate with each other as intended. Although bus subsystem 5102 is shown schematically as a single bus, alternative aspects of the bus subsystem may utilize multiple buses. Bus subsystem 5102 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. For example, such architectures may include an Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, which can be implemented as a Mezzanine bus manufactured to the IEEE P5186.1 standard.
  • ISA Industry Standard Architecture
  • MCA Micro Channel Architecture
  • EISA Enhanced ISA
  • VESA Video Electronics Standards Association
  • PCI Peripheral Component Interconnect
  • Processing unit 5104 which can be implemented as one or more integrated circuits (e.g., a conventional microprocessor or microcontroller), controls the operation of computer system 5100.
  • processors may be included in processing unit 5104. These processors may include single core or multicore processors.
  • processing unit 5104 may be implemented as one or more independent processing units 5132 and/or 5134 with single or multicore processors included in each processing unit.
  • processing unit 5104 may also be implemented as a quad-core processing unit formed by integrating two dual-core processors into a single chip.
  • processing unit 5104 can execute a variety of programs in response to program code and can maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed can be resident in processing unit 5104 and/or in storage subsystem 5118. Through suitable programming, processing unit 5104 can provide various functionalities described above.
  • Computer system 5100 may additionally include a processing acceleration unit 5106, which can include a digital signal processor (DSP), a special-purpose processor, and/or the like.
  • DSP digital signal processor
  • I/O subsystem 5108 may include user interface input devices and user interface output devices.
  • User interface input devices may include a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices.
  • User interface input devices may include, for example, motion sensing and/or gesture recognition devices such as the Microsoft Kinect® motion sensor that enables users to control and interact with an input device, such as the Microsoft Xbox® 360 game controller, through a natural user interface using gestures and spoken commands.
  • User interface input devices may also include eye gesture recognition devices such as the Google Glass® blink detector that detects eye activity (e.g., ‘blinking’ while taking pictures and/or making a menu selection) from users and transforms the eye gestures as input into an input device (e.g., Google Glass®). Additionally, user interface input devices may include voice recognition sensing devices that enable users to interact with voice recognition systems (e.g., Siri® navigator), through voice commands.
  • eye gesture recognition devices such as the Google Glass® blink detector that detects eye activity (e.g., ‘blinking’ while taking pictures and/or making a menu selection) from users and transforms the eye gestures as input into an input device (e.g., Google Glass®).
  • user interface input devices may include voice recognition sensing devices that enable users to interact with voice recognition systems (e.g., Siri® navigator), through voice commands.
  • voice recognition systems e.g., Siri® navigator
  • User interface input devices may also include, without limitation, three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices. Additionally, user interface input devices may include, for example, medical imaging input devices such as computed tomography, magnetic resonance imaging, position emission tomography, medical ultrasonography devices. User interface input devices may also include, for example, audio input devices such as MIDI keyboards, digital musical instruments and the like.
  • User interface output devices may include a display subsystem, indicator lights, or non-visual displays such as audio output devices, etc.
  • the display subsystem may be a cathode ray tube (CRT), a flat-panel device, such as that using a liquid crystal display (LCD) or plasma display, a projection device, a touch screen, and the like.
  • CTR cathode ray tube
  • LCD liquid crystal display
  • plasma display a projection device
  • touch screen a touch screen
  • output device is intended to include all possible types of devices and mechanisms for outputting information from computer system 5100 to a user or other computer.
  • user interface output devices may include, without limitation, a variety of display devices that visually convey text, graphics and audio/video information such as monitors, printers, speakers, headphones, automotive navigation systems, plotters, voice output devices, and modems.
  • Computer system 5100 may comprise a storage subsystem 5118 that comprises software elements, shown as being currently located within a system memory 5110.
  • System memory 5110 may store program instructions that are loadable and executable on processing unit 5104, as well as data generated during the execution of these programs.
  • system memory 5110 may be volatile (such as random access memory (RAM)) and/or non-volatile (such as read-only memory (ROM), flash memory, etc.)
  • RAM random access memory
  • ROM read-only memory
  • system memory 5110 may include multiple different types of memory, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • BIOS basic input/output system
  • BIOS basic input/output system
  • BIOS basic routines that help to transfer information between elements within computer system 5100, such as during start-up, may typically be stored in the ROM.
  • system memory 5110 also illustrates application programs 5112, which may include client applications, Web browsers, mid-tier applications, relational database management systems (RDBMS), etc., program data 5114, and an operating system 5116.
  • operating system 5116 may include various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems, a variety of commercially-available UNIX® or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems, the Google Chrome® OS, and the like) and/or mobile operating systems such as iOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, and Palm® OS operating systems.
  • Storage subsystem 5118 may also provide a tangible computer-readable storage medium for storing the basic programming and data constructs that provide the functionality of some aspects.
  • Software programs, code modules, instructions that when executed by a processor provide the functionality described above may be stored in storage subsystem 5118. These software modules or instructions may be executed by processing unit 5104.
  • Storage subsystem 5118 may also provide a repository for storing data used in accordance with the present invention.
  • Storage subsystem 5118 may also include a computer-readable storage media reader 5120 that can further be connected to computer-readable storage media 5122. Together and, optionally, in combination with system memory 5110, computer-readable storage media 5122 may comprehensively represent remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing, storing, transmitting, and retrieving computer-readable information.
  • Computer-readable storage media 5122 containing code, or portions of code can also include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information.
  • This can include tangible, non-transitory computer-readable storage media such as RAM, ROM, electronically erasable programmable ROM (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible computer readable media.
  • this can also include nontangible, transitory computer-readable media, such as data signals, data transmissions, or any other medium which can be used to transmit the desired information and which can be accessed by computer system 5100.
  • computer-readable storage media 5122 may include a hard disk drive that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive that reads from or writes to a removable, nonvolatile magnetic disk, and an optical disk drive that reads from or writes to a removable, nonvolatile optical disk such as a CD ROM, DVD, and Blu-Ray® disk, or other optical media.
  • Computer-readable storage media 5122 may include, but is not limited to, Zip® drives, flash memory cards, universal serial bus (USB) flash drives, secure digital (SD) cards, DVD disks, digital video tape, and the like.
  • Computer-readable storage media 5122 may also include, solid-state drives (SSD) based on non-volatile memory such as flash-memory based SSDs, enterprise flash drives, solid state ROM, and the like, SSDs based on volatile memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and flash memory based SSDs.
  • SSD solid-state drives
  • volatile memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and flash memory based SSDs.
  • MRAM magnetoresistive RAM
  • hybrid SSDs that use a combination of DRAM and flash memory based SSDs.
  • the disk drives and their associated computer-readable media may provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for computer system 5100.
  • Communications subsystem 5124 provides an interface to other computer systems and networks. Communications subsystem 5124 serves as an interface for receiving data from and transmitting data to other systems from computer system 5100. For example, communications subsystem 5124 may enable computer system 5100 to connect to one or more devices via the Internet.
  • communications subsystem 5124 can include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular telephone technology, advanced data network technology, such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi (IEEE 802.28 family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components.
  • RF radio frequency
  • communications subsystem 5124 can provide wired network connectivity (e.g., Ethernet) in addition to or instead of a wireless interface.
  • communications subsystem 5124 may also receive input communication in the form of structured and/or unstructured data feeds 5126, event streams 5128, event updates 5151, and the like on behalf of one or more users who may use computer system 5100.
  • communications subsystem 5124 may be configured to receive unstructured data feeds 5126 in real-time from users of social media networks and/or other communication services such as Twitter® feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS) feeds, and/or real-time updates from one or more third party information sources.
  • RSS Rich Site Summary
  • communications subsystem 5124 may also be configured to receive data in the form of continuous data streams, which may include event streams 5128 of real time events and/or event updates 5151, that may be continuous or unbounded in nature with no explicit end.
  • continuous data streams may include, for example, sensor data applications, financial tickers, network performance measuring tools (e.g. network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like.
  • Communications subsystem 5124 may also be configured to output the structured and/or unstructured data feeds 5126, event streams 5128, event updates 5151, and the like to one or more databases that may be in communication with one or more streaming data source computers coupled to computer system 5100.
  • Computer system 5100 can be one of various types, including a handheld portable device (e.g., an iPhone® cellular phone, an iPad® computing tablet, a PDA), a wearable device (e.g., a Google Glass® head mounted display), a PC, a workstation, a mainframe, a kiosk, a server rack, or any other data processing system.
  • a handheld portable device e.g., an iPhone® cellular phone, an iPad® computing tablet, a PDA
  • a wearable device e.g., a Google Glass® head mounted display
  • PC personal computer
  • workstation e.g., a workstation
  • mainframe e.g., a mainframe
  • kiosk e.g., a server rack
  • server rack e.g., a server rack, or any other data processing system.

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Abstract

La présente invention concerne des systèmes, des dispositifs et des procédés faisant intervenir des arbres de discours. Selon certains aspects, un système crée un arbre de discours en identifiant des unités élémentaires de discours dans du texte. L'arbre de discours comprend des nœuds, chaque nœud non terminal représentant une relation rhétorique entre deux unités élémentaires de discours et chaque nœud terminal étant associé à une unité élémentaire de discours. Le système identifie, dans une phrase de référence de l'arbre de discours, une relation rhétorique de type élaboration ou jointure. Le système sélectionne une phrase candidate présentant un plus haut score de généralisation syntactique d'un ensemble de scores de généralisation syntactique. Le système identifie une relation sémantique correspondant à la phrase candidate. La relation sémantique correspond à un mot dans la phrase candidate et définit un rôle dans la phrase candidate. Le système remplace, dans l'arbre de discours, la relation rhétorique par une relation rhétorique mise à jour, qui correspond à la relation sémantique, créant ainsi un arbre de discours mis à jour.
PCT/US2021/035515 2020-06-26 2021-06-02 Analyse de discours améliorée WO2021262408A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114662469A (zh) * 2022-02-25 2022-06-24 北京百度网讯科技有限公司 情感分析方法、装置、电子设备及存储介质

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10679011B2 (en) * 2017-05-10 2020-06-09 Oracle International Corporation Enabling chatbots by detecting and supporting argumentation

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10679011B2 (en) * 2017-05-10 2020-06-09 Oracle International Corporation Enabling chatbots by detecting and supporting argumentation

Non-Patent Citations (26)

* Cited by examiner, † Cited by third party
Title
"Summarising Text for Intelligent Communication", vol. 79, 1995, DAGSTUHL SEMINAR REPORT
AJJOURYAMENWEI- FAN CHENJOHANNES KIESELHENNING WACHSMUTHBENNO STEIN: "Unit Segmentation of Argumentative Texts", PROCEEDINGS OF THE 4TH WORKSHOP ON ARGUMENT MINING, 2017, pages 118 - 128
BAR-HAIMROY LILACH EDELSTEINCHARLES JOCHIMNOAM SLONIM: "Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization", PROCEEDINGS OF THE 4TH WORKSHOP ON ARGUMENT MINING, 2017, pages 32 - 38
BAUMEISTER, R. F.BUSHMAN, B. J.: "Social psychology and human nature", INTERNATIONAL EDITION, 2010
BENGIOYOSHUAREJEAN DUCHARMEPASCAL VINCENTCHRISTIAN JANVIN: "A neural probabilistic language model", J. MACH. LEARN. RES., vol. 3, 2003, pages 1137 - 1155
COHEN P. R.LEVESQUE, H. J.: "Intention is choice with commitment", ARTIFICIAL INTELLIGENCE, vol. 42, 1990, pages 213 - 261
COULTHARD, R. M.BRAZIL D., EXCHANGE STRUCTURE: DISCOURSE ANALYSIS MONOGRAPHS NO. 5. BIRMINGHAM, 1979
DE BONIMARCO: "Using logical relevance for question answering", JOURNAL OF APPLIED LOGIC, VOLUME 5, 1 March 2007 (2007-03-01), pages 92 - 103
GALITSKY BORIS A ET AL: "Inferring the semantic properties of sentences by mining syntactic parse trees", DATA & KNOWLEDGE ENGINEERING, vol. 81, 28 July 2012 (2012-07-28), pages 21 - 45, XP028947586, ISSN: 0169-023X, DOI: 10.1016/J.DATAK.2012.07.003 *
GALITSKY BORIS: "Learning parse structure of paragraphs and its applications in search", ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE., vol. 32, June 2014 (2014-06-01), GB, pages 160 - 184, XP055838131, ISSN: 0952-1976, DOI: 10.1016/j.engappai.2014.02.013 *
GALITSKY, BILVOVSKY, D.KUZNETSOV SO: "Rhetoric Map of an Answer to Compound Queries Knowledge Trail Inc", ACL, 2015, pages 681 - 686
GROSZBARBARA J.SIDNERCANDACE L.: "Attentions, Intentions and the Structure of Discourse", COMPUTATIONAL LINGUISTICS, vol. 12, no. 3, 1986, pages 175 - 204
JINDALLIUOPINION SPAMANALYSIS: "Department of Computer Science", UNIVERSITY OF ILLINOIS AT CHICAGO, 2008
JOTY, SHAFIQ RGIUSEPPE CARENINIRAYMOND T NGYASHAR MEHDAD: "Combining intra-and multi- sentential rhetorical parsing for document-level discourse analysis", ACL, vol. 1, 2013, pages 486 - 496, XP055497546
KARIN KIPPERANNA KORHONENNEVILLE RYANTMARTHA PALMER, LANGUAGE RESOURCES AND EVALUATION, vol. 42, no. 1, March 2008 (2008-03-01)
KARIN KIPPERANNA KORHONENNEVILLE RYANTMARTHA PALMER: "A Large-scale Classification of English Verbs", LANGUAGE RESOURCES AND EVALUATION JOURNAL, vol. 42, no. 1, pages 21 - 40, XP019571103
LAWRENCEJOHNCHRIS REED: "Mining Argumentative Structure from Natural Language text using Automatically Generated Premise-Conclusion Topic Models", PROCEEDINGS OF THE 4TH WORKSHOP ON ARGUMENT MINING, 2017, pages 39 - 48
LITMAN, D. L.ALLEN, J. F.: "A plan recognition model for subdialogues in conversation", COGNITIVE SCIENCE, vol. 11, 1987, pages 163 - 2
MANNWILLIAMSANDRA THOMPSON: "Rhetorical structure theory: Towards a functional theory of text organization", TEXT-INTERDISCIPLINARY JOURNAL FOR THE STUDY OF DISCOURSE, vol. 8, no. 3, 1988, pages 243 - 281
MANNWILLIAMTHOMPSONSANDRA: "Rhetorical structure theory: A Theory of Text organization", TEXT-INTERDISCIPLINARY JOURNAL FOR THE STUDY OF DISCOURSE, vol. 8, no. 3, 1988, pages 243 - 281
PELDSZUS, A.STEDE, M.: "From Argument Diagrams to Argumentation Mining in Texts: A Survey", INT. J OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE, vol. 7, no. 1, 2013, pages 1 - 31
POPESCUVLADIMIRJEAN CAELENCORNELIU BURILEANU: "Logic-Based Rhetorical Structuring for Natural Language Generation in Human-Computer Dialogue", LECTURE NOTES IN COMPUTER SCIENCE VOLUME, vol. 4629, 2007, pages 309 - 317
RADEV, DRAGOMIR R.HONGYAN JINGMALGORZATA BUDZIKOWSKA.: "Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies", PROCEEDINGS OF THE 2000 NAACL-ANLPWORKSHOP ON AUTOMATIC SUMMARIZATION, vol. 4, 2000
SEARLE, J. R.: "Speech acts: an essay in the philosophy of language", 1969, CAMBRIDGE UNIVERSITY PRESS
WANG, W.SU, J.TAN, C. L.: "Kernel Based Discourse Relation Recognition with Temporal Ordering Information", PROCEEDINGS OF THE 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, 2010
ZHU WENWU ET AL: "On a Chatbot Conducting Virtual Dialogues", PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT , CIKM '19, vol. 4, 3 November 2019 (2019-11-03), New York, New York, USA, pages 2925 - 2928, XP055838126, ISBN: 978-1-4503-6976-3, Retrieved from the Internet <URL:https://dl.acm.org/doi/pdf/10.1145/3357384.3357842> [retrieved on 20210906], DOI: 10.1145/3357384.3357842 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114662469A (zh) * 2022-02-25 2022-06-24 北京百度网讯科技有限公司 情感分析方法、装置、电子设备及存储介质

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