CN111797199A - Method and device for analyzing legal information based on event chain structure - Google Patents
Method and device for analyzing legal information based on event chain structure Download PDFInfo
- Publication number
- CN111797199A CN111797199A CN202010540456.2A CN202010540456A CN111797199A CN 111797199 A CN111797199 A CN 111797199A CN 202010540456 A CN202010540456 A CN 202010540456A CN 111797199 A CN111797199 A CN 111797199A
- Authority
- CN
- China
- Prior art keywords
- event
- chain structure
- information
- identification
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000012549 training Methods 0.000 claims description 64
- 238000002372 labelling Methods 0.000 claims description 40
- 238000004590 computer program Methods 0.000 claims description 6
- 230000003993 interaction Effects 0.000 claims description 6
- 230000001364 causal effect Effects 0.000 claims description 4
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 description 11
- 238000010586 diagram Methods 0.000 description 8
- 239000000463 material Substances 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 230000035622 drinking Effects 0.000 description 5
- 230000007704 transition Effects 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000000153 supplemental effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- 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
- G06F16/3331—Query processing
- G06F16/334—Query execution
- G06F16/3344—Query execution using natural language analysis
-
- 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
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
-
- 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
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/18—Legal services
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Tourism & Hospitality (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Artificial Intelligence (AREA)
- Technology Law (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Machine Translation (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method and a device for analyzing legal information based on an event chain structure, wherein the method comprises the following steps: acquiring a complete event chain structure; analyzing legal information and legal concepts of each event node according to the complete event chain structure to obtain text answers corresponding to each event node; combining each current event node information with the next event node information, and determining a combined text answer; and sending the combined text answers, the legal concepts related to each event node and the related questions to the user. According to the invention, the legal information and legal concepts of each node are analyzed according to the complete event chain structure, and combined with the information of the next event node to obtain the text answer, so that deeper natural language understanding can be achieved, the obtained legal concepts and related problems are closer to the user intention, and the intelligent understanding capability and reasoning capability are realized.
Description
Technical Field
The invention relates to the field of natural language understanding, in particular to a method and a device for analyzing legal information based on an event chain structure.
Background
Currently, in the field of natural language understanding, a common method for identifying or classifying event information described in natural language is to label event classification at sentence level, train a classifier through a neural network, or determine the type of a target event through a trigger word in a target sentence and a context word of the trigger word, perform event identification, further obtain a whole event chain structural body, and understand user text and semantics. However, the prior art has the disadvantages that information cognition is not sufficient, a conclusion is not accurate enough, and process information of semantic turning change cannot be acquired, so that the conditions that pushed answers obtained after a user inquires about each event node and explanations of related legal concepts in an identification text are inaccurate or experience is poor occur.
The prior art has the following disadvantages:
1. information awareness is insufficient. In the event classification task at the sentence level or the event classification task based on the event trigger word, only the information describing the core event or some key event in the information is found under the condition that the classification is correctly recognized, and the natural language description process is quite possible to involve a plurality of events and the transition process of the events. For example, "a friend drinks after birthday, drives after drinking to hurt one person, and then escapes" a sentence describes the whole process of drinking, drunk driving and hit-and-run, and if event trigger word recognition or event classification task recognition is based on event trigger word recognition, whether drunk driving or hit-and-run is recognized, the problem described by the original sentence is not enough to be accurately defined in the vertical field of law. Since drunk driving and hit-and-run are legally illegal behaviors, and are necessary to be respectively responsible and punished, if only one of the behaviors is identified, the semantics can not be understood accurately obviously. Drunk driving must be identified, the two events are escaped, and an event chain about the escape after drunk driving is formed, so that the information is fully identified, and the downstream higher-level tasks are facilitated.
2. The conclusions are not accurate enough. The task targets of finding event information and event-related element information at sentence level are not enough to support in a logically complex scene, especially when there are event state change, long-text long-distance event description and turning process, the problems of event transition relation, event structure state change and the like may be involved, if only the core events are identified or classified, even if the identification task is not wrong, the conclusion is not necessarily correct.
3. The process information of the semantic turning change cannot be acquired. In the prior art, the identification of events is limited to a conclusive target, the attention to a procedural target is insufficient, and the semantic information expressed by the natural language is not only represented by a conclusive event, but also depends on the development, state, logic break and the like of the event.
Disclosure of Invention
In order to solve the problems that the pushed answers obtained after a user inquires about each event node and the explanations of related legal concepts in an identification text are inaccurate or have poor experience, the invention provides a method and a device for analyzing legal information based on an event chain structure.
In a first aspect, the present invention provides a method for analyzing legal information based on an event chain structure, the method comprising:
acquiring a complete event chain structure;
analyzing legal information and legal concepts of each event node according to the complete event chain structure to obtain text answers corresponding to each event node;
combining each current event node information with the next event node information, and determining a combined text answer;
and sending the combined text answers, the legal concepts related to each event node and the related questions to the user.
Further, acquiring a complete event chain structure includes:
obtaining semantic data to be identified;
semantic information data to be recognized are added into a preset event chain structure recognition model to obtain a model recognition result; the preset event chain structure recognition model is formed by training a training set comprising event data, event state data, event relation data and semantic role data;
determining information to be supplemented of an event chain structure according to the model identification result and a vertical domain knowledge base;
and analyzing and reasoning the event nodes of the supplemented event chain structure to obtain the logic key nodes of the event chain structure, and establishing a complete event chain structure.
Further, inputting the information of the event to be identified into a preset event chain structure identification model to obtain a model identification result; the preset event chain structure recognition model is trained by a training set comprising event data, event state data, event relation data and semantic role data, and comprises the following steps:
carrying out comprehensive labeling of event labeling, event state labeling, event relation labeling and semantic role labeling on current semantic data to be identified by utilizing a vertical domain knowledge base of an expert knowledge system;
establishing a meta-event labeling training set, a state identification labeling training set, an intention identification labeling training set and a role identification labeling training set according to event labeling data, event state labeling data, event relation labeling data and semantic role labeling data;
and establishing a multi-task learning model according to the meta-event label training set, the state identification label training set, the intention identification label training set and the role identification label training set to obtain a model identification result.
Further, determining the information to be supplemented of the event chain structure according to the model identification result and the vertical domain knowledge base comprises the following steps:
constructing an event triple according to an event identification result, an event relation identification result and a state identification result in the model identification result;
adding role identification information into the event triple, and combining the role identification information with intention identification information to obtain a preliminary event chain structure;
and matching the preliminary event chain structure with the vertical domain knowledge base to perform event chain matching, and supplementing the current preliminary event chain structure.
Further, analyzing and reasoning the event nodes of the supplemented event chain structure to obtain the logic key nodes of the event chain structure, and establishing a complete event chain structure comprises:
analyzing and reasoning event nodes for the preliminary event chain structure, and determining event node supplementary information;
sending the event node supplementary information to a user and carrying out multi-round interaction with the user to determine a logic key node forming an event chain;
and adding the logic key node into the event chain structure to determine the complete event chain structure.
Further, the multitask learning model comprises: the system comprises an event identification model, an event state judgment model, an event relation identification model and a role identification model.
Further, establishing a multi-task learning model according to the meta-event label training set, the role identification label training set, the intention identification label training set and the state identification label training set, and obtaining a model identification result comprises the following steps:
identifying event time sequence, positive or negative state words in a meta-event labeling training set and a state identification labeling training set by using an event identification model and an event state judgment model, and determining event state information;
identifying causal relationships, time sequence relationships and correlation relationships of events in the meta-event annotation training set by using an event identification model and an event relationship identification model, and determining event triples;
carrying out hierarchical annotation on the role information in the meta-event annotation training set by using a role identification model and a logical structure of a law three-level theory to determine a target object;
and obtaining a model identification result according to the event state information, the event triple and the target object.
In a second aspect, the present invention provides an apparatus for analyzing legal information based on an event chain structure, the apparatus comprising:
the complete event chain structure acquisition module is used for acquiring a complete event chain structure;
the analysis module is used for analyzing the legal information and legal concepts of each event node according to the complete event chain structure to obtain text answers corresponding to each event node;
the text answer obtaining module is used for combining each current event node information with the next event node information and determining a combined text answer;
and the sending module is used for sending the combined text answers, the legal concepts related to each event node and the related questions to the user.
In a third aspect, the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for analyzing legal information based on an event chain structure provided in the first aspect when executing the program.
In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for analyzing legal information based on an event chain structure provided in the first aspect.
According to the invention, the legal information and legal concepts of each node are analyzed according to the complete event chain structure, and combined with the information of the next event node to obtain the text answer, so that deeper natural language understanding can be achieved, the obtained legal concepts and related problems are closer to the user intention, and the intelligent understanding capability and reasoning capability are realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for analyzing legal information based on an event chain structure according to an embodiment of the present invention;
FIG. 2 is a diagram of a data comprehensive annotation and construction of a data annotation standard based on an expert knowledge system according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a structure of a multi-task learning model according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an event chain preliminarily constructed according to a recognition result according to an embodiment of the present invention;
fig. 5 is a schematic diagram of event chain and user interaction supplemental event chain information provided in an embodiment of the present invention;
FIG. 6 is a block diagram of an apparatus for analyzing legal information based on an event chain structure according to an embodiment of the present invention;
fig. 7 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The method is characterized in that the event knowledge system is difficult to summarize and sort up because of the fact that the event recognition in a wide field is aimed at, and the direct relationship system of the event is more complicated and complicated, cannot be represented by limited representation, and cannot limit the world view and the field view. Secondly, because the identification of the event state information is not well defined and paid attention in a wide field at present, the change of the event state in the legal field, whether the event state occurs or not, and whether the existing information has very important influence on the problems of legal responsibility confirmation, legal concept application and the like. Third, there is no legal expert knowledge support in the vertical field, and the knowledge in the field can not be systematically limited based on the three-layer theory in the vertical field, so that the recognition accuracy is reduced. In order to solve the above problem, an embodiment of the present invention provides a method for analyzing legal information based on an event chain structure, as shown in fig. 1, the method includes:
step S101, acquiring a complete event chain structure;
step S102, according to the complete event chain structure, analyzing legal information and legal concepts of each event node to obtain text answers corresponding to each event node;
step S103, combining each current event node information with the next event node information, and determining a combined text answer;
and step S104, sending the combined text answers, the legal concepts related to each event node and the related questions to the user.
And generating question and answer answers, related concepts, similar questions and the like under corresponding conditions according to legal expert information and legal knowledge information corresponding to each node on the event chain to form a complete answer to be fed back to the user.
More specifically, the obtained complete event chain is analyzed for legal information and legal concepts of each event node to obtain answers and related concepts corresponding to each event node, combined with information of the next node, the combined text answer is determined, the answer corresponding to the text is answered to the user, and the legal concepts and related questions related to each event node are pushed to the user. See table 1 below.
TABLE 1
According to the embodiment of the invention, the legal information and legal concepts of each node are analyzed according to the complete event chain structure, and the legal information and legal concepts are combined with the information of the next event node to obtain the text answer, so that deeper natural language understanding can be achieved, the obtained legal concepts and related problems are closer to the user intention, and the intelligent understanding capability and reasoning capability are realized.
Based on the content of the above embodiments, as an alternative embodiment: acquiring a complete event chain structure comprises the following steps:
obtaining semantic data to be identified;
semantic information data to be recognized are added into a preset event chain structure recognition model to obtain a model recognition result; the preset event chain structure recognition model is formed by training a training set comprising event data, event state data, event relation data and semantic role data;
determining information to be supplemented of an event chain structure according to the model identification result and a vertical domain knowledge base;
and analyzing and reasoning the event nodes of the supplemented event chain structure to obtain the logic key nodes of the event chain structure, and establishing a complete event chain structure.
Specifically, as shown in fig. 2, an expert knowledge system is used to establish a vertical domain knowledge base to which current semantic data to be identified belongs, that is, a knowledge system for subdividing a domain is formulated together through an expert role system of a law three-level theory, and includes an event system, an event relation system, a role knowledge system and the like in the domain, so as to form a unified annotation interpretation specification and a guidance file for annotating work of a knowledge engineer. The method comprises the steps that an expert knowledge system of a vertical field is established by related experts, through the establishment of the expert knowledge system, the world view, the field view and the expert view knowledge in the field are combed, information such as related events, intentions and roles in the field range is established, and a data comprehensive labeling task is carried out under the knowledge system. The annotation task comprises the comprehensive annotation of event annotation, event state annotation, event relation annotation (intention annotation), semantic role annotation and the like. As shown in table 2, table 2 is data labeled under the knowledge system established in the road traffic field:
TABLE 2
And generating a specific training set and a test set for the labeled data according to different algorithm scene requirements, wherein the labeled data mainly comprises an entity identification data set, a role identification data set, an event relation data set and the like, and the entity identification data set, the role identification data set, the event relation data set and the like are used as training sets respectively corresponding to subtasks of multi-task learning. The detailed training set comprises a meta-event marking training set, a state identification marking training set, an intention identification marking training set and a role identification marking training set.
And establishing a multi-task learning model according to the meta-event annotation training set, the state identification annotation training set, the intention identification annotation training set and the role identification annotation training set, inputting the training sets into a sharing layer to obtain the relationship among the identified meta-event, role, intention and state as shown in fig. 3, and simultaneously supervising the tasks mutually, thereby improving the training efficiency and reducing the cost of data production and model production.
Matching the relation among the identified meta-events, roles, intentions and states with a vertical domain knowledge base, generating event chain data by adopting a generation and verification mode according to the vertical domain knowledge base of an expert knowledge system, verifying and correcting an event chain according to industry knowledge, and determining information to be supplemented of an event chain structure; and then analyzing and reasoning the event nodes of the supplemented event chain structure to obtain the logic key nodes of the event chain structure and establish a complete event chain structure.
The embodiment of the invention constructs a set of target, effective and sequential identification scheme by utilizing the legal expert knowledge system on the basis of strictly combining with the legal expert knowledge system, adopts a multi-task learning model framework on the combination of specific tasks, reduces the implementation cost and the construction difficulty of identifying an event chain structure, can more accurately extract text semantic event information by combining with a vertical domain knowledge base, accurately analyzes the event state and the relationship between events, accurately distinguishes the roles of a subject and an object, and further determines a complete event chain structure.
Based on the content of the above embodiments, as an alternative embodiment: determining the information to be supplemented of the event chain structure according to the model identification result and the vertical domain knowledge base comprises the following steps:
constructing an event triple according to an event identification result, an event relation identification result and a state identification result in the model identification result;
adding role identification information into the event triple, and combining the role identification information with intention identification information to obtain a preliminary event chain structure;
and matching the preliminary event chain structure with the vertical domain knowledge base to perform event chain matching, and supplementing the current preliminary event chain structure.
Specifically, as shown in fig. 4, an event triple is established according to an event identification result, an event relationship identification result, and a state identification result in a model identification result, role identification information is added to the event triple, and is combined with intention identification information to obtain a combined preliminary event chain structure, the preliminary event chain structure is subjected to event chain similarity matching with a vertical domain knowledge base to match a corresponding event chain, and a logically and state most similar event chain is obtained through a relationship between sub-events and sub-events included in the event chain and a state transition process to represent an event and state structure of a current statement.
Based on the content of the above embodiments, as an alternative embodiment: analyzing and reasoning the event nodes of the supplemented event chain structure to obtain the logic key nodes of the event chain structure, and establishing the complete event chain structure comprises the following steps:
analyzing and reasoning event nodes for the preliminary event chain structure, and determining event node supplementary information;
sending the event node supplementary information to a user and carrying out multi-round interaction with the user to determine a logic key node forming an event chain;
and adding the logic key node into the event chain structure to determine the complete event chain structure.
Specifically, the event nodes are analyzed and inferred for the preliminary event chain structure, event node supplementary information is determined, the supplementary event node information is used as a target to feed back to a user for multi-round interaction with the user, multi-round interaction is carried out according to the feedback of the event chain, the supplementary event chain forms required logic key nodes, and perfect event chain information is obtained, so that the obtained event chain information is more comprehensive to the user and closer to the intention of the user. See in particular fig. 5.
Based on the content of the above embodiments, as an alternative embodiment: establishing a multi-task learning model according to the meta-event label training set, the role identification label training set, the intention identification label training set and the state identification label training set, and obtaining a model identification result comprises the following steps:
identifying event time sequence, positive or negative state words in a meta-event labeling training set and a state identification labeling training set by using an event identification model and an event state judgment model, and determining event state information;
identifying causal relationships, time sequence relationships and correlation relationships of events in the meta-event annotation training set by using an event identification model and an event relationship identification model, and determining event triples;
carrying out hierarchical annotation on the role information in the meta-event annotation training set by using a role identification model and a logical structure of a law three-level theory to determine a target object;
and obtaining a model identification result according to the event state information, the event triple and the target object.
Specifically, the multitask learning model comprises: the system comprises an event identification model, an event state judgment model, an event relation identification model and a role identification model. Using the event recognition model and the event state judgment model, recognizing event timing, positive or negative state words in the meta-event labeling training set and the state recognition labeling training set, and determining event state information, for example: the "office has I committed me to the proposal material, I committed, what should be done next," identifies that the "committed proposal material" and "committed" express "proposal material" events have ended. Whether the logic is identified or not is to judge whether the event occurs or not, whether the event exists or not, for example, in the phrase "i have not already submitted the case material", only identifying the "case material" is unable to correctly understand the meaning of the text, and the state "has no" of the "case material" event must be identified to correctly understand the true meaning of the text. The recognition of the event state aims to fully understand the event state of the user description information so as to help clearly grasp the stage and the state of the event in the user expression semantics, and the included information and the negative information are the premise of fully recognizing and understanding the semantic information.
And identifying the causal relationship, the time sequence relationship and the correlation relationship of the events in the meta-event labeling training set by using the event identification model and the event relationship identification model, and determining the event triples. Examples are: for example, the method comprises the steps of identifying an event of drinking, driving, causing traffic accidents and bearing what responsibility, identifying the event of drinking, driving, causing traffic accidents and escaping, belonging to a time sequence relation between drinking and driving, causing the accidents and escaping to belong to a cause-effect relation, judging and identifying the event relation, mainly constructing an event triple, constructing an event chain according to the composition relation of the triple, and understanding the sequence of the events in user information, the reasons and the results of the events and the process of transferring the events.
And carrying out hierarchical annotation on the role information in the meta-event annotation training set by utilizing a role identification model and a logical structure of a law three-level theory, and determining a target object. A role recognition model is established for the labeled data, and based on the main logical structure of the law three-level theory, layered labeling of world view-field view-expert view is carried out, for example, in the phrase of 'personal tax payment standard', for the word of 'personal tax', layered labeling is carried out (tax type-obtained tax-personal obtained tax) and then intraformational homogeneity is increased, so that the variation degree of an observed value is reduced, the sampling error of each layer is reduced, meanwhile, from the perspective of world view, the large concept of object-person is not confused, the recognition error is favorably reduced, and the layered role labeling recognition effect actually exceeds 90%. The identification of semantic roles is beneficial to judging the subject-object relationship in the event and judging the roles of all subjects involved in the text in the event so as to determine the objects of the targets of responsibility confirmation, attribution and the like.
And associating the identified event state information, the event triple and the target object to obtain a model identification result.
According to still another aspect of the present invention, an apparatus for analyzing legal information based on an event chain structure is provided in an embodiment of the present invention, and referring to fig. 6, fig. 6 is a block diagram of an apparatus for analyzing legal information based on an event chain structure provided in an embodiment of the present invention. The device is used for analyzing the legal information based on the event chain structure provided by the embodiment of the invention in the embodiments. Therefore, the description and definition in the method for analyzing legal information based on the event chain structure provided in the embodiments of the present invention in the foregoing embodiments can be used for understanding the execution modules in the embodiments of the present invention.
The device includes:
an obtain complete event chain structure module 601, configured to obtain a complete event chain structure;
the analysis module 602 is configured to analyze legal information and legal concepts of each event node according to the complete event chain structure to obtain a text answer corresponding to each event node;
a text answer obtaining module 603, configured to combine each current event node information with next event node information, and determine a combined text answer;
and a sending module 604, configured to send the combined text answer, the legal concepts related to each event node, and the related question to the user.
Specifically, the specific process of each module in the apparatus of this embodiment to implement its function may refer to the related description in the corresponding method embodiment, and is not described herein again.
According to the embodiment of the invention, the legal information and legal concepts of each node are analyzed according to the complete event chain structure, and the legal information and legal concepts are combined with the information of the next event node to obtain the text answer, so that deeper natural language understanding can be achieved, the obtained legal concepts and related problems are closer to the user intention, and the intelligent understanding capability and reasoning capability are realized.
Fig. 7 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 7, the electronic device includes: a processor 701, a memory 702, and a bus 703;
the processor 701 and the memory 702 complete mutual communication through the bus 703; the processor 701 is configured to call the program instructions in the memory 702 to execute the method for analyzing the legal information based on the event chain structure, which is provided by the foregoing embodiments, for example, the method includes: acquiring a complete event chain structure; analyzing legal information and legal concepts of each event node according to the complete event chain structure to obtain text answers corresponding to each event node; combining each current event node information with the next event node information, and determining a combined text answer; and sending the combined text answers, the legal concepts related to each event node and the related questions to the user.
Embodiments of the present invention provide a non-transitory computer readable storage medium having stored thereon a computer program that, when executed by a processor, performs the steps of a method for analyzing legal information based on an event chain structure. Examples include: acquiring a complete event chain structure; analyzing legal information and legal concepts of each event node according to the complete event chain structure to obtain text answers corresponding to each event node; combining each current event node information with the next event node information, and determining a combined text answer; and sending the combined text answers, the legal concepts related to each event node and the related questions to the user.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, the principle and the implementation of the present invention are explained by applying the specific embodiments in the present invention, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for analyzing legal information based on an event chain structure, the method comprising:
acquiring a complete event chain structure;
analyzing legal information and legal concepts of each event node according to the complete event chain structure to obtain text answers corresponding to each event node;
combining each current event node information with the next event node information, and determining a combined text answer;
and sending the combined text answers, the legal concepts related to each event node and the related questions to the user.
2. The method of claim 1, wherein obtaining the complete event chain structure comprises:
obtaining semantic data to be identified;
the semantic information data to be recognized is added into a preset event chain structure recognition model to obtain a model recognition result; the preset event chain structure recognition model is trained by a training set comprising event data, event state data, event relation data and semantic role data;
determining information to be supplemented of an event chain structure according to the model identification result and a vertical domain knowledge base;
and analyzing and reasoning the event nodes of the supplemented event chain structure to obtain the logic key nodes of the event chain structure, and establishing a complete event chain structure.
3. The method according to claim 2, wherein the event information to be recognized is input into a preset event chain structure recognition model, so as to obtain a model recognition result; the preset event chain structure recognition model is trained by a training set comprising event data, event state data, event relation data and semantic role data, and comprises the following steps:
carrying out comprehensive labeling of event labeling, event state labeling, event relation labeling and semantic role labeling on current semantic data to be identified by utilizing a vertical domain knowledge base of an expert knowledge system;
establishing a meta-event labeling training set, a state identification labeling training set, an intention identification labeling training set and a role identification labeling training set according to event labeling data, event state labeling data, event relation labeling data and semantic role labeling data;
and establishing a multi-task learning model according to the meta-event label training set, the state identification label training set, the intention identification label training set and the role identification label training set to obtain a model identification result.
4. The method of claim 2, wherein the determining the information to be supplemented by the event chain structure according to the model recognition result and the vertical domain knowledge base comprises:
constructing an event triple according to an event identification result, an event relation identification result and a state identification result in the model identification result;
adding role identification information into the event triple, and combining the role identification information with intention identification information to obtain a preliminary event chain structure;
and performing event chain matching on the preliminary event chain structure and a vertical domain knowledge base, and supplementing the current preliminary event chain structure.
5. The method according to claim 4, wherein the analyzing and reasoning the event nodes for the supplemented event chain structure to obtain the logical key nodes of the event chain structure, and the establishing of the complete event chain structure comprises:
analyzing and reasoning event nodes for the preliminary event chain structure, and determining event node supplementary information;
sending the event node supplementary information to a user and carrying out multi-round interaction with the user to determine a logic key node forming an event chain;
and adding the logic key node into the event chain structure to determine a complete event chain structure.
6. The method of claim 3, wherein the multitask learning model comprises: the system comprises an event identification model, an event state judgment model, an event relation identification model and a role identification model.
7. The method of claim 6, wherein the establishing a multi-task learning model according to the meta-event annotation training set, the role identification annotation training set, the intention identification annotation training set, and the state identification annotation training set, and obtaining a model identification result comprises:
identifying event time sequence, positive or negative state words in a meta-event labeling training set and a state identification labeling training set by using the event identification model and the event state judgment model, and determining event state information;
identifying event causal relationship, time sequence relationship and correlation relationship in a meta-event labeling training set by using the event identification model and the event relationship identification model, and determining an event triple;
carrying out hierarchical annotation on the role information in the meta-event annotation training set by utilizing the role identification model and the logical structure of the law three-level theory to determine a target object;
and obtaining a model identification result according to the event state information, the event triple and the target object.
8. An apparatus for analyzing legal information based on an event chain structure, the apparatus comprising:
the complete event chain structure acquisition module is used for acquiring a complete event chain structure;
the analysis module is used for analyzing the legal information and legal concepts of each event node according to the complete event chain structure to obtain a text answer corresponding to each event node;
the text answer obtaining module is used for combining each current event node information with the next event node information and determining a combined text answer;
and the sending module is used for sending the combined text answers, the legal concepts related to each event node and the related questions to the user.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for analyzing legal information based on an event chain structure as claimed in any one of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for analyzing legal information based on an event chain structure as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010540456.2A CN111797199B (en) | 2020-06-12 | 2020-06-12 | Method and device for analyzing legal information based on event chain structure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010540456.2A CN111797199B (en) | 2020-06-12 | 2020-06-12 | Method and device for analyzing legal information based on event chain structure |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111797199A true CN111797199A (en) | 2020-10-20 |
| CN111797199B CN111797199B (en) | 2021-02-23 |
Family
ID=72804351
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010540456.2A Active CN111797199B (en) | 2020-06-12 | 2020-06-12 | Method and device for analyzing legal information based on event chain structure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111797199B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112463941A (en) * | 2020-12-04 | 2021-03-09 | 南京擎盾信息科技有限公司 | Event chain-based multi-round question and answer method and device |
| CN113239146A (en) * | 2021-05-12 | 2021-08-10 | 平安科技（深圳）有限公司 | Response analysis method, device, equipment and storage medium |
| CN115878815A (en) * | 2022-11-29 | 2023-03-31 | 深圳擎盾信息科技有限公司 | Legal document judgment result prediction method, device and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104240164A (en) * | 2014-09-29 | 2014-12-24 | 南京提坦信息科技有限公司 | Legal consulting method and legal consulting system based on big data analysis |
| US20160364500A1 (en) * | 2015-06-15 | 2016-12-15 | International Business Machines Corporation | Synthetic Events to Chain Queries Against Structured Data |
| US9934249B2 (en) * | 2014-06-03 | 2018-04-03 | Conduent Business Machines Services, Llc | Systems and methods for context-aware and personalized access to visualizations of road events |
| CN109002431A (en) * | 2018-07-20 | 2018-12-14 | 吴怡 | Legal opinion generates system and production method |
| CN110188346A (en) * | 2019-04-29 | 2019-08-30 | 浙江工业大学 | A kind of network security bill part intelligence analysis method based on information extraction |
| CN110232447A (en) * | 2019-04-28 | 2019-09-13 | 杭州实在智能科技有限公司 | Legal case depth reasoning method |
| CN110674840A (en) * | 2019-08-22 | 2020-01-10 | 中国司法大数据研究院有限公司 | Multi-party evidence association model construction method based on Bayesian network and evidence chain extraction method and device |
| CN110990544A (en) * | 2019-11-01 | 2020-04-10 | 上海百事通信息技术股份有限公司 | Intelligent question-answering platform for legal consultation |
-
2020
- 2020-06-12 CN CN202010540456.2A patent/CN111797199B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9934249B2 (en) * | 2014-06-03 | 2018-04-03 | Conduent Business Machines Services, Llc | Systems and methods for context-aware and personalized access to visualizations of road events |
| CN104240164A (en) * | 2014-09-29 | 2014-12-24 | 南京提坦信息科技有限公司 | Legal consulting method and legal consulting system based on big data analysis |
| US20160364500A1 (en) * | 2015-06-15 | 2016-12-15 | International Business Machines Corporation | Synthetic Events to Chain Queries Against Structured Data |
| CN109002431A (en) * | 2018-07-20 | 2018-12-14 | 吴怡 | Legal opinion generates system and production method |
| CN110232447A (en) * | 2019-04-28 | 2019-09-13 | 杭州实在智能科技有限公司 | Legal case depth reasoning method |
| CN110188346A (en) * | 2019-04-29 | 2019-08-30 | 浙江工业大学 | A kind of network security bill part intelligence analysis method based on information extraction |
| CN110674840A (en) * | 2019-08-22 | 2020-01-10 | 中国司法大数据研究院有限公司 | Multi-party evidence association model construction method based on Bayesian network and evidence chain extraction method and device |
| CN110990544A (en) * | 2019-11-01 | 2020-04-10 | 上海百事通信息技术股份有限公司 | Intelligent question-answering platform for legal consultation |
Non-Patent Citations (2)
| Title |
|---|
| PLAKORN ACHANANUPARP等: ""Answer diversification for complex question answering on the web"", 《PAKDD 10:PROCEEDINGS OF THE 14TH PACIFIC-ASIA CONFERENCE ON ADVANCES IN KONWLEDGE DISCOVERY AND DATA MINING》 * |
| 康雨培: ""基于事件链的网络舆情知识库模型构建研究"", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112463941A (en) * | 2020-12-04 | 2021-03-09 | 南京擎盾信息科技有限公司 | Event chain-based multi-round question and answer method and device |
| CN112463941B (en) * | 2020-12-04 | 2024-05-17 | 南京擎盾信息科技有限公司 | Multi-round question and answer method and device based on event chain |
| CN113239146A (en) * | 2021-05-12 | 2021-08-10 | 平安科技（深圳）有限公司 | Response analysis method, device, equipment and storage medium |
| CN113239146B (en) * | 2021-05-12 | 2023-07-28 | 平安科技（深圳）有限公司 | Response analysis method, device, equipment and storage medium |
| CN115878815A (en) * | 2022-11-29 | 2023-03-31 | 深圳擎盾信息科技有限公司 | Legal document judgment result prediction method, device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111797199B (en) | 2021-02-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111797199B (en) | Method and device for analyzing legal information based on event chain structure | |
| US20240086793A1 (en) | Omnichannel, intelligent, proactive virtual agent | |
| Winkler et al. | Automatic classification of requirements based on convolutional neural networks | |
| CN110991163B (en) | Document comparison and analysis method and device, electronic equipment and storage medium | |
| CN111159356B (en) | Knowledge graph construction method based on teaching content | |
| CN111309910A (en) | Text information mining method and device | |
| CN112989761A (en) | Text classification method and device | |
| CN111797233B (en) | Method and device for identifying event chain structure based on vertical field | |
| CN110188190B (en) | Dialogue analysis method, device, server and readable storage medium | |
| US20220138414A1 (en) | Meta-knowledge fine tuning method and platform for multi-task language model | |
| CN115098665A (en) | Method, device and equipment for expanding session data | |
| CN110019772B (en) | Text emotion classification method and system | |
| CN106844448A (en) | A kind of recognition methods of Chinese event fact and system | |
| Beaver et al. | On the care and feeding of virtual assistants: Automating conversation review with AI | |
| Suresh et al. | Identifying teamwork indicators in an online collaborative problem-solving task: A text-mining approach | |
| CN111563162A (en) | MOOC comment analysis system and method based on text emotion analysis | |
| CN114637849B (en) | Legal relation cognition method and system based on artificial intelligence | |
| CN116109142B (en) | Dangerous waste supervision method, system and device based on artificial intelligence | |
| Tian | Construction of Computer English Corpus Assisted by Internet of Things Information Perception and Interaction Technology | |
| Liu | Classification of English Educational Resources Information Based on Mobile Learning Using Cognitive Web Service. | |
| TWI689843B (en) | Intelligent appraisal system and method therof | |
| Meng et al. | Design of Question Answering System for Railway Electricity Accidents | |
| CN111382234A (en) | Reply providing method and device based on customer service | |
| Alzoubi et al. | Generative Artificial Intelligence Technology for Systems Engineering Research: Contribution and Challenges | |
| Rong | A Practical Exploration of the Integration of Civics into Student Education and Management in Colleges and Universities Empowered by Information Integration Technology in the Context of Three-Whole Parenting |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |