WO2017010652A1 - Procédé pour questions et réponses automatiques et dispositif associé - Google Patents

Procédé pour questions et réponses automatiques et dispositif associé Download PDF

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Publication number
WO2017010652A1
WO2017010652A1 PCT/KR2016/002275 KR2016002275W WO2017010652A1 WO 2017010652 A1 WO2017010652 A1 WO 2017010652A1 KR 2016002275 W KR2016002275 W KR 2016002275W WO 2017010652 A1 WO2017010652 A1 WO 2017010652A1
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query
sentence
query sentence
response
natural language
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PCT/KR2016/002275
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English (en)
Korean (ko)
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이근배
박선영
김병수
심효섭
한상도
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포항공과대학교 산학협력단
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Publication of WO2017010652A1 publication Critical patent/WO2017010652A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/242—Query formulation
    • G06F16/243—Natural language query formulation

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  • the present invention relates to an automatic query response method and apparatus, and more particularly, to an automatic query response method and apparatus for grasping an information request from a natural language query sentence and extracting information suitable for the information request from a database based on a knowledge base. It is about.
  • the knowledge base is composed of triples of ⁇ objects, relationships and entities>, which are atomic forms of fragmentary knowledge. This triple can be used as a resource for solving the user's information needs.
  • the source of information in the automatic query response method based on the existing technology of information retrieval is large text. Because the paragraphs retrieved from these large tests are provided as a response to an information request, an automated query response method based on information retrieval can add text to broaden the resolution of the information request, but the accuracy of the response is relatively low. low.
  • the knowledgebase-based automatic query response method is relatively accurate because a suitable response is retrieved from a highly structured knowledgebase.
  • the range of the knowledge base-based automatic query response method is relatively narrow. Because of these characteristics, knowledge base based automatic query response and information retrieval based automatic query response can complement each other.
  • An object of the present invention for solving the above problems is to provide an automatic query response method and apparatus for grasping an information request from a natural language query sentence and extracting information suitable for the information request from a database based on a knowledge base. .
  • the automatic query response method comprises the steps of: splitting the input natural language query sentence into one or more phrases; Converting a word included in each of the phrases into a formal language; Generating a first query sentence by combining the phrases converted into a formal language according to a predefined grammar relating to the formal language; And extracting a first response sentence for the first query sentence from a database composed of a plurality of query sentence-response sentences expressed in the formal language, based on a database composed of a plurality of sample query sentences. Extracting a query pattern corresponding to the natural language query sentence from a predefined query pattern; Generating a second query sentence by applying a template corresponding to the extracted query pattern to the natural language query sentence; And extracting a second response sentence for the second query sentence.
  • the automatic query response method may further include displaying the first response sentence, but additionally displaying the second response sentence.
  • the dividing into the phrase may include dividing the natural language query sentence into word units; And generating the phrase by combining the words, but the existing word may be omitted in the combining process.
  • the formal language may express the natural language query sentence in a formalized structure that is not sensitive to word order or vocabulary changes.
  • the word may be converted into an attribute and an entity name of the formal language.
  • the generating of the first query sentence may include: generating one or more query sentence candidates expressed in the formal language; And selecting, as the first query sentence, a query sentence candidate having the highest sum of similarities evaluated as air information of a formal language included in the query sentence candidate.
  • a candidate evaluation model trained based on a database composed of pairs of natural language query sentence-correct query sentences may be used.
  • the extracting of the query pattern may include whether the natural language query sentence includes a predefined phenotype, and whether the chunk includes a predefined vocabulary when the natural language query sentence is analyzed in chunks.
  • the query pattern may be extracted in consideration of at least one of the number of chunks and the type of the chunks.
  • the template may include: a slot information template for extracting slot information about a formal language corresponding to the natural language query sentence; And a query template for converting the natural language query sentence into the second query sentence using the slot information.
  • the automatic query response apparatus generates a first query sentence expressed in a formal language from an input natural language query sentence, and comprises a database composed of a plurality of pairs of query sentence-response sentences expressed in a formal language.
  • a semantic parsing module that extracts a first response sentence from the first query sentence from the first query sentence; And generating a second query sentence by applying a query template included in the query pattern corresponding to the natural language query sentence to the natural language query sentence among predefined query patterns, and generating a second response to the second query sentence from the database. It includes a query pattern template module to extract the.
  • the semantic parsing module may include: a parser that divides the input natural language query sentence into word units and combines the words to generate one or more phrases, and omits existing words in the recombination process; A candidate generation module for converting the phrase into a formal language phrase expressed in a formal language and generating one or more query sentence candidates by combining the formal language syntax based on a predefined grammar relating to the formal language; A candidate evaluation module that selects, as a first query sentence, a query sentence candidate having the highest sum of similarities evaluated as air information of a formal language included in the query sentence candidate; And an output module that extracts a first response sentence for the first query sentence.
  • the query pattern template module may include a pattern extraction module configured to extract a query pattern corresponding to the natural language query sentence from a predefined query pattern; A template application module for generating a second query sentence by applying a template included in the query pattern to the natural language query sentence; And an output module for extracting a second response to the second query sentence.
  • the automatic query response device may further include a display unit that primarily displays the first response sentence and additionally displays the second response sentence.
  • the formal language may express the natural language query sentence in a formalized structure that is not sensitive to word order or vocabulary changes.
  • the candidate generation module may convert the word into an attribute and an entity name of a formal language.
  • the output module is a query that conforms to the SPARQL standard, which is a standard that can query the database consisting of a plurality of query sentence-response sentences expressed in the formal language of the first query sentence or the second query sentence. Can be converted to a sentence.
  • SPARQL standard is a standard that can query the database consisting of a plurality of query sentence-response sentences expressed in the formal language of the first query sentence or the second query sentence. Can be converted to a sentence.
  • the candidate evaluation module may use a candidate evaluation model learned through a database composed of pairs of the natural language query sentence and the correct sentence sentence to evaluate the query sentence candidate.
  • the pattern extraction module may include whether the natural language query sentence includes a predefined phenotype, whether the chunk includes a predefined vocabulary when the natural language query sentence is analyzed in chunks, and the chunk.
  • the query pattern may be extracted in consideration of at least one of the number of and the type of the chunk.
  • the template may include: a slot information template for extracting slot information about a formal language corresponding to the natural language query sentence; And a query template for converting the natural language query sentence into the second query sentence using the slot information.
  • a response having a high degree of suitability can be output by grasping a user's information request even in a variation such as a change in word order or a change in a vocabulary of a natural language query sentence.
  • FIG. 1 is a block diagram of an automatic query response device according to an embodiment of the present invention.
  • FIG. 2 is a flowchart illustrating an automatic query response method according to an embodiment of the present invention.
  • first and second may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.
  • the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.
  • FIG. 1 is a block diagram of an automatic query response device according to an embodiment of the present invention.
  • an automatic query response system includes an automatic query response device 100, a database 200, a candidate evaluation model trainer 310, and a candidate evaluation model 320.
  • the database 200 includes a database composed of a pair of query sentence-response sentences expressed in a formal language, a database composed of a plurality of sample query sentences, a database composed of a pair of natural language query sentence-correct query sentences, and a natural language-attribute. It may include a dictionary database such as a dictionary or entity name dictionary.
  • the database 200 according to the embodiment of the present invention means a database in the form of a knowledge base.
  • the automatic query response apparatus 100 includes a semantic parsing module 110 and a query pattern template module 120.
  • the semantic parsing module 110 generates a first query sentence expressed in a formal language from an input natural language query sentence, and generates a first query sentence from a database composed of a plurality of pairs of query sentence-response sentences expressed in the formal language.
  • the first response sentence may be extracted.
  • the query pattern template module 120 generates a second query sentence by applying the query template included in the query pattern corresponding to the natural language query sentence to the natural language sentence from among the predefined query patterns, and from the database 200, the second query sentence. A second response to may be extracted.
  • the process of extracting the first response sentence by the semantic parsing module 110 and the process of extracting the second response sentence by the query pattern template module 120 may occur simultaneously or sequentially regardless of the order.
  • the automatic query response apparatus 100 may simultaneously display or sequentially display the first response sentence and the second response sentence to the user using a display unit (not shown).
  • the automatic query response apparatus 100 may display a second response sentence that is distinguished from the first response sentence according to the input natural language query sentence. Accordingly, the user may identify the first response sentence as a response to the natural language query sentence, and may check and refer to the second response sentence if the first response sentence is not suitable as a response. Therefore, from the user's point of view, the template module 120 may play an additional role of extracting a second response regarding the natural language query sentence.
  • the semantic parsing module 110 may include a parser 111, a candidate generation module 112, a candidate evaluation module 113, and an output module 114.
  • the semantic parsing module 110 may derive a formal semantic expression from a natural language query sentence.
  • Formal semantic representations of natural language query sentences may be represented by a formal language.
  • the formal language may express a natural language query sentence in a formal structure that is not sensitive to word order or vocabulary changes.
  • the semantic parsing module 110 may generate the first query sentence expressed in the formal language from the natural language query sentence using the formal language.
  • the semantic parsing module 110 generates a first query sentence expressed in a formal language from an input natural language query sentence, and generates a first query from a database composed of a plurality of query sentence-response sentences expressed in the formal language. The first response to the sentence may be extracted.
  • the parser 111 divides the input natural language query sentence into word units and combines each word again to generate one or more phrases. In this process, the parser 111 may omit an existing word included in a natural language query sentence. That is, words that do not significantly affect the meaning of words constituting the natural language query sentence may be omitted.
  • the phrase refers to a sequence of words forming a natural language query sentence. In generating the phrase, the order of the words appearing in the natural language query sentence should be maintained. For example, if a natural query statement is "who is the wife of abraham lincoln?", A phrase such as "who", "is”, ..., "who is” may be generated.
  • the candidate generation module 112 may generate one or more query sentence candidates by converting the generated syntax into a formal language syntax expressed in a formal language, and combining the formal language syntax based on a grammar relating to a predefined formal language. have. That is, the natural language query sentence divided into a plurality of phrases in the parser module 111 may be converted into a knowledge base vocabulary, which is a formal language corresponding to a portion corresponding to an entity name and a portion corresponding to an attribute in the candidate generation module 112. Can be.
  • a word having a higher similarity evaluated by air (co-occurrence) information included in the dictionary database may be selected.
  • air refers to a phenomenon in which a word and a word are used together in a single document or sentence. In other words, form, morpheme, phoneme, phoneme and so on appear in the same sentence, phrase, or word without grammatical deviation.
  • the grammatical elements that have a favorable relationship are called air expressions and such a relationship is called air relations.
  • one or more query sentence candidates corresponding to the formal semantic expression may be generated according to a compound grammar between knowledge base vocabularies, which are grammars for the formal language.
  • the dictionary database used for the knowledge base lexical conversion may include a natural language-property dictionary and an entity name dictionary.
  • Natural language-property dictionaries are dictionary databases that represent natural language phrases and knowledgebase attribute vocabulary. Information is extracted from a large amount of text by an information extraction tool, and the extracted information and the actual knowledge base are aligned to generate a pair of natural language phrases and attribute vocabulary, and the obtained air information is used for similarity evaluation.
  • the entity name dictionary is a dictionary database constructed by collecting a knowledge base entity name vocabulary.
  • a concatenation rule dictionary is a dictionary that includes a few derivation rules for synthesizing from the minimum unit form semantic representation of a statement to a formal semantic representation representing the entire query statement.
  • the candidate evaluation module 113 may select, as the first query sentence, a query sentence candidate having the highest sum of similarities evaluated as air information of a formal language included in the query sentence candidate. That is, the candidate evaluation module 113 has a formal semantic expression that is evaluated as having the highest similarity as the sum of similarity previously evaluated by the air information with respect to the formal semantic expression of the query sentence candidate generated by the candidate generation module 112.
  • the query sentence may be selected as the first query sentence.
  • the candidate evaluation module 113 may use a candidate evaluation model learned through a database composed of pairs of natural language question sentence-correct query sentences to evaluate a query sentence candidate.
  • the output module 114 converts the first query sentence or the second query sentence into a query sentence that conforms to the SPARQL standard, which is a standard for querying a database composed of a plurality of query statement-response sentences expressed in a formal language. Can be.
  • the candidate evaluation model trainer 310 trains the candidate evaluation model 320. That is, the candidate evaluation model trainer 310 may play a role of learning a model for evaluating a candidate of formal semantic expression in a machine learning method from a database composed of pairs of natural language sentence-correct query sentences.
  • the query pattern template module 120 extracts a response from the database by extracting a query pattern corresponding to the input natural language query sentence from a plurality of sample query sentences and applying a template included in the query pattern to the natural language query sentence. do.
  • the query pattern template module 120 may include a pattern extraction module 121, a template application module 122, and an output module 123.
  • the pattern extraction module 121 checks the feature values from the natural language query sentence and extracts a query pattern in which the feature values match.
  • the pattern extraction module 121 is a feature value, and whether the natural language query sentence includes a predefined phenotype, and if the natural language query sentence is analyzed in chunks, whether the chunk includes a predefined vocabulary.
  • the query pattern may be extracted in consideration of at least one of the number of chunks and the type of chunks.
  • the chunk may be composed of words in a sentence that are semantically or grammatically related to each other.
  • a chunk means a sequence of words in a sentence including a core vocabulary representing a function or a role.
  • the template application module 122 may convert a natural language query sentence into a second query sentence expressed in a formal language by applying a template corresponding to the extracted query pattern to the natural language query sentence.
  • the template applied here may include a slot information template for extracting slot information about a formal language corresponding to a natural language query sentence and a query template for converting a natural language query sentence into a second query sentence using slot information. .
  • FIG. 2 is a flowchart illustrating an automatic query response method according to an embodiment of the present invention.
  • the parser 111 may divide the input natural language query sentence into word units and combine the divided words to generate one or more phrases (S211).
  • the parser 111 may omit the existing word in the recombination process.
  • the candidate generation module 112 may generate one or more query sentence candidates by converting a word included in each phrase into a formal language and combining the formal language phrases based on the grammar of the predefined formal language (S212, S213).
  • the candidate evaluation module 113 may select, as the first query sentence, a query sentence candidate having the highest sum of similarities evaluated as the cumulative change number of the formal language included in the query sentence candidate.
  • the output module 114 may extract a first response sentence for the first query sentence from a database composed of a plurality of query sentence-response characters expressed in a formal language (S214).
  • the automatic query response device 100 may perform the following procedure. That is, the automatic query response apparatus 100 may primarily display the first response sentence, but additionally display the second response sentence. The displaying of the second response sentence may be at the user's option.
  • the pattern extraction module 121 may extract a query pattern corresponding to a natural language query sentence from a predefined query pattern based on a database composed of a plurality of sample query sentences (S221).
  • the database consisting of a plurality of sample query sentences may be implemented in the form of a pattern dictionary.
  • the pattern dictionary may be manually implemented in advance.
  • One entry in the pattern dictionary includes a sentence pattern rule, a slot information template, and a query template.
  • Sentence pattern rules are further divided into lexical patterns, chunk type patterns, and chunk patterns.
  • Vocabulary patterns are rules that determine matching patterns through the presence of direct vocabulary.
  • the chunk pattern is a rule for determining a matching pattern through the number and type of chunks obtained as a result of chunking a natural language query sentence.
  • the pattern in the chunk is a rule for determining a matching pattern through whether or not a vocabulary having a part of speech included in the rule exists among the elements in the chunk.
  • the template application module 122 may generate a second query sentence by applying a template included in the extracted query pattern to the natural language query sentence (S222).
  • the output module 123 may extract a second response sentence for the second query sentence from a database composed of a plurality of query sentence-response sentences expressed in a formal language (S223).
  • the automatic query response device 100 displays the first response to the first query sentence using the semantic parsing module 110 with respect to the input natural language query sentence, and additionally.
  • the second response to the second query sentence may be displayed using the query pattern template module 120 for the natural language query sentence.
  • the method of using the semantic parsing module 110 is suitable for a natural language query sentence in which various components are combined, and the method of using the query pattern template module 120 is suitable for a simple form of natural language query sentence. Therefore, according to the present invention, the response can be extracted by different methods according to the form of the natural language query sentence, and the response corresponding to the information request of the user can be output.
  • the methods according to the invention can be implemented in the form of program instructions that can be executed by various computer means and recorded on a computer readable medium.
  • Computer-readable media may include, alone or in combination with the program instructions, data files, data structures, and the like.
  • the program instructions recorded on the computer readable medium may be those specially designed and constructed for the present invention, or may be known and available to those skilled in computer software.
  • Examples of computer readable media include hardware devices that are specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like.
  • Examples of program instructions include machine language code, such as produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like.
  • the hardware device described above may be configured to operate with at least one software module to perform the operations of the present invention, and vice versa.

Abstract

L'invention concerne un dispositif pour questions et réponses automatiques et un procédé associé. Le dispositif pour questions et réponses automatiques comporte: un module d'analyse sémantique servant à générer une première phrase interrogative exprimée dans un langage formel à partir d'une phrase interrogative introduite en langage naturel, et à extraire d'une base de données une première phrase de réponse à la première phrase interrogative; et un module de modèles de schémas de questions servant à générer une deuxième phrase interrogative en appliquant à une phrase interrogative en langage naturel un modèle de question compris dans un schéma de question auquel correspond la phrase interrogative en langage naturel, parmi des schémas de questions prédéterminés, et à extraire de la base de données une deuxième réponse à la deuxième phrase interrogative. En conséquence, une demande d'informations de la part d'un utilisateur peut être identifiée et une réponse d'une adéquation élevée peut être délivrée malgré des variations comme un changement dans l'ordre des mots ou le remplacement d'un mot dans une phrase interrogative en langage naturel.
PCT/KR2016/002275 2015-07-15 2016-03-08 Procédé pour questions et réponses automatiques et dispositif associé WO2017010652A1 (fr)

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