CN113222251A - Case dispute focus-based auxiliary judgment result prediction method and system - Google Patents
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Abstract
The invention discloses an auxiliary judgment result prediction method based on case dispute focus, which comprises the steps of firstly constructing a dispute focus knowledge graph and a court trial model; secondly, inducing appeal dissimilarity points of both parties according to the dispute focus knowledge graph, and pushing evidence related to dispute points to a court to complete preparedness of the court; finally, based on natural language processing technology, according to dispute debates of both parties and court trial, matching the judgment result with the factual statement according to the set court trial model, and predicting case results, the invention also provides an auxiliary judgment result prediction system based on the dispute focus of cases, which comprises a data entry module, a preparation module before court, a court trial module and an output module.
Description
Technical Field
The invention relates to the technical field of judicial judgment, in particular to an auxiliary judgment result prediction method and system based on case dispute focus.
Background
In the judicial field, after litigation requesters submit litigation materials, the resultsof the litigation can be deduced according to similar case judgment in the litigation materials based on the submitted litigation materials, and the traditional methods mainly comprise the following steps:
1. similar cases are searched by matching with keywords, the method has low search precision and few search results, and under the result of the search method, many cases do not contain the keywords but have words which are synonymous or similar to the keywords, and if the keywords are not selected properly, a large number of cases occur, which is unfavorable for comparison of the results.
2. The method is proved to be very limited in application by deducing legal results by using logic rules, can only make accurate reasoning according to the content specified by a clockwork spring, and cannot be applied to the ever-changing legal cases in reality.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides an auxiliary judgment result prediction method based on case dispute focus, which can overcome the defects of the prior art.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows:
an auxiliary judgment result prediction method based on case dispute focus comprises the following steps:
s1, constructing a dispute focus knowledge graph and a court trial model;
s2, summarizing alleviation dissimilarity points of both parties according to the dispute focus knowledge graph, pushing evidence related to dispute points to a court, and completing preparation before the court;
and S3, adopting confidence for dispute points and evidences of both parties according to the court trial debates of both parties, matching the judgment results with the factual statements according to the set court trial model, and predicting case results.
Further, the construction method of the dispute focus knowledge graph comprises the following steps:
s11, identifying and extracting object entities, attributes and the relationship among the object entities by using a natural language processing and text semantic analysis method;
s12, performing knowledge fusion on the identified and extracted object entities and attributes, and eliminating honor and wrong objects through entity connection and just merging disambiguation concepts;
and S13, through knowledge fusion, giving definite definition to the concepts and the connection between the concepts in a formalized mode, and constructing a knowledge graph related to the current situation.
Further, the court trial model in step S1 includes: legal affirmation model, factual affirmation model and automatic calculation model of claims and repayment.
Further, the specific method of the court preparation in step S1 is as follows:
s21, collecting the information in the case handling system, the dispute mediation system and the electronic file system;
s22, extracting and summarizing the dissimilarity points of the parties in the aspects of case facts and requirements through judging litigation requests of the two parties of the original quilt, and forming a court trial outline;
s23, pushing the complaint and evidence materials related to the dispute points to the judge, assisting the judge to understand the case, and completing the preparation of antecourt data.
Further, the method for summarizing the similarities and differences between the parties in the case facts and the appeal in step S22 includes:
s221, training and optimizing the model by data, understanding content semantics and identifying contradiction claims of both sides;
s222, adopting contradiction problem identification to refine and summarize contradictory advices of both sides of the dispute, and adopting dispute collection point classification accuracy and the credit acquisition rate of other judicial staff such as lawyers, judges, inspection officers and the like to verify;
s223, the reinforcement learning and increment learning method based on big data continuously improves the semantic analysis and contradiction claim detection effects, refines the dispute focus, and simultaneously checks whether the legal documents are wrongly summarized or omit the dispute focus.
Further, the step S3 further includes the following steps:
s31, fusing linguistic features into word vectors representing learning by using a deep learning method based on a contradictory assertion recognition technology of text inclusion recognition, and establishing a bidirectional RNN neural network model;
s32, constructing a fine-grained opinion analysis model based on sequence labeling, and fusing word vectors, parts of speech and dependency relationship linguistic features of the text;
s33, learning modification and semantic information of the text, and constructing a time sequence labeling model;
and S34, extracting attribute entities to judge the emotion polarity of the text, analyzing and identifying the advices of both sides, and obtaining the refining information of the dispute focus.
On the other hand, the invention discloses an auxiliary judgment result prediction system based on case dispute focus, which comprises a data entry module, a pre-court preparation module, a court trial module and an output module, wherein,
the data entry module is used for constructing and storing a dispute focus knowledge graph and a court trial model;
a pre-court preparation module, which is used for inducing the appeal dissimilarity points of both parties based on the prior advertised litigation request according to the dispute focus knowledge graph, pushing the evidence related to the dispute points to the court and completing the pre-court preparation;
the court trial module is used for collecting confidence of disputed points and evidences of both parties according to the court trial debates of both parties of the parties based on a natural language processing technology, matching judgment results with factual statements according to a set court trial model and predicting case results;
and the output module is used for outputting the court trial documents.
The invention has the beneficial effects that: the problem of theory analysis is solved by utilizing a plurality of information such as the relationship of each element of the case, legal logic, expert experience, similar cases and the like in the case knowledge graph, the prediction of case results is realized based on a natural language processing technology, and the court trial efficiency is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flowchart illustrating a case dispute focus-based auxiliary referee result prediction method according to an embodiment of the present invention.
FIG. 2 is a schematic structural diagram of an auxiliary referee result prediction system based on case dispute focus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention belong to the protection scope of the present invention, and for the convenience of understanding the above technical solutions of the present invention, the above technical solutions of the present invention are described in detail below by specific use modes.
As shown in FIG. 1, the auxiliary judgment result prediction method based on case dispute focus according to the embodiment of the invention comprises the steps of firstly constructing a dispute focus knowledge base and a court trial model.
The method for constructing the dispute focus knowledge graph comprises the following steps: firstly, identifying and extracting object entities, attributes and relationships among the object entities by using a natural language processing and text semantic analysis method; then, carrying out knowledge fusion on the identified and extracted objects and attributes, and eliminating synonymous and wrong objects by combining an ambiguity elimination concept through entity connection and knowledge; and finally, through knowledge fusion, giving clear definition to the concepts and the relation between the concepts in a formalized mode, and constructing a knowledge graph related to the current family case.
The court trial model comprises: legal affirmation model, factual affirmation model and automatic calculation model of claims and repayment.
The construction of the dispute focus knowledge graph and the court trial model is to perform character disassembly and element extraction on the speaking content of the historical case, combine the words with case handling experience of a judge, construct all dispute focuses related to the cases into the knowledge graph by taking the case as the center, and form a speaking knowledge base for various cases through knowledge processing and natural language processing.
And taking the case as a center, and constructing a knowledge graph by all knowledge related to the case. The knowledge covered includes: data in the existing database of the case, historical resolution documents related to the case or similar, and language materials such as legal rules and regulations applicable to the case. Firstly, identifying and extracting object entities, attributes and relationships among the object entities by using a natural language processing and text semantic analysis method; then, carrying out knowledge fusion, eliminating ambiguous concepts through entity linking and knowledge merging, and eliminating redundant and wrong objects; and finally, through knowledge processing, giving a clear definition to the concepts and the relation between the concepts in a formalized mode, constructing a knowledge graph of the case, and providing data support for deep intelligent reasoning of judgment theory analysis.
And (4) inducing the appeal dissimilarity points of the two parties of the parties according to the dispute focus knowledge graph based on the originally advertised litigation request, pushing evidence related to the dispute points to the court, and completing the preparation before the court.
The concrete method for the court preparation comprises the following steps: firstly, summarizing information in a case handling system, a dispute mediation system and an electronic file system; then through the judgment of litigation requests of both parties of the original quilt, the dissimilarity points of the parties in the aspects of case facts and requirements are extracted and summarized to form a court trial outline; finally, the complaint and evidence materials related to the dispute points are pushed to the judge, so that the judge is assisted to understand the case, and the antecourt data preparation is completed.
The method for extracting and inducing the dissimilarity of the parties in the aspects of case facts and appeal comprises the steps of firstly, training and optimizing a model by data, further understanding content semantics and identifying contradiction claims of both sides; then, adopting contradiction problem identification to refine and summarize contradiction advices of both sides of the dispute, and adopting dispute collection point classification accuracy and the adoption rate of lawyers, judges, inspection officers and other judicial staff to verify; and finally, based on a big data reinforcement learning and incremental learning method, the semantic analysis and contradiction claim detection effects are continuously improved, the dispute focus is refined, and whether the legal documents are wrongly summarized or the dispute focus is omitted or not is verified.
The information in the case handling system, the dispute mediation system and the electronic file system is gathered, the dissimilarity points of the two parties of the parties concerned about the case fact and the appeal are automatically abstracted and summarized through the judgment of the litigation requests of the two parties of the original defendant (such as the comparison analysis of contradiction claims such as loan relation, loan condition and the like), a court trail outline is formed, the complaint state and evidence material related to the dispute point are pushed to the judge, the judge is assisted to understand the case, and the preparation of the pre-court data is completed.
Based on natural language processing technology, according to the court trial debate of both parties, the dispute point and evidence of both parties are informed, and according to the set court trial model, the judgment result is matched with the factual statement, and the case result is predicted. The contradiction claim identification technology based on text implication identification utilizes a deep learning method to integrate linguistic features into word vectors representing learning, constructs a fine-grained opinion analysis model based on sequence annotation by establishing a bidirectional RNN neural network model, integrates the word vectors, the part of speech and the dependency relationship linguistic features of texts, learns the modification and semantic information of the texts, constructs a time sequence annotation model, extracts attribute entities to judge the emotional polarity of the texts, and analyzes and identifies the advocates and the advocates to obtain the refining information of the dispute focus.
A judge adopts confidence for dispute focus and evidence of two parties through court trial debate, the system realizes judgment result prediction, applicable legal interpretation (including case law and law statement analysis, association law and law permutation and combination) and judgment result logical reasoning based on case facts according to a legal approval model, a fact approval model and an indemnity automatic calculation model, and the judgment result is matched with a fact statement based on a natural language processing technology to predict case results and improve the court trial efficiency.
The judgment theory analysis comprises the aspects of case fact identification, evidence adoption, dispute focus analysis, applicable law interpretation, logical reasoning, judgment results and the like. The problem of theory analysis is solved by utilizing a plurality of information such as the relationship of each element of the case, legal logic, expert experience, similar cases and the like in the case knowledge graph. For simple reasoning, a first-order predicate logic mode is adopted, predicates are expressed through logic operation symbols, and logic and constraint conditions of relation reasoning are set to realize the reasoning of simple relations; for complex entity relations, a graph reasoning method based on a knowledge graph is adopted to find paths existing between a source node and a target node, and possible relations between the two nodes are presumed through semantic information contained in the paths. And meanwhile, deep learning is introduced, knowledge representation learning is carried out by using models such as TransE and the like, entities and relations in the knowledge map are mapped into vectors in a low-dimensional dense space, calculation and reasoning are carried out, reasoning rules are induced, reasoning and prediction of judgment results are carried out, and auxiliary decision support can be provided for judge cases.
On the other hand, as shown in fig. 2, the invention also discloses an auxiliary referee result prediction system based on case dispute focus, which comprises a data entry module, a database module and a database module, wherein the data entry module is used for constructing and storing a dispute focus knowledge graph and a court trial model; a pre-court preparation module, which is used for inducing the appeal dissimilarity points of both parties based on the prior advertised litigation request according to the dispute focus knowledge graph, pushing the evidence related to the dispute points to the court and completing the pre-court preparation; the court trial module is used for collecting confidence of disputed points and evidences of both parties according to the court trial debates of both parties of the parties based on a natural language processing technology, matching judgment results with factual statements according to a set court trial model and predicting case results; and the output module is used for outputting the court trial documents.
In summary, with the technical solution of the present invention, the problem of theoretical analysis is solved by utilizing a plurality of information such as the relationship of each element of the case, legal logic, expert experience, and similar cases in the case knowledge graph. For simple reasoning, a first-order predicate logic mode is adopted, predicates are expressed through logic operation symbols, and logic and constraint conditions of relation reasoning are set to realize the reasoning of simple relations; for complex entity relations, a graph reasoning method based on a knowledge graph is adopted to search for paths existing between a source node and a target node, the possible relation between the two nodes is presumed through semantic information contained in the paths, and based on a natural language processing technology, case results are predicted, and court trial efficiency is improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. An auxiliary judgment result prediction method based on case dispute focus is characterized by comprising the following steps:
s1, constructing a dispute focus knowledge graph and a court trial model;
s2, summarizing the appeal dissimilarity points of both parties according to the dispute focus knowledge graph, pushing evidence related to dispute points to a court, and completing preparation before the court;
and S3, adopting confidence for dispute points and evidences of both parties according to the court trial debates of both parties, matching the judgment results with the factual statements according to the set court trial model, and predicting case results.
2. The case dispute focus-based auxiliary referee result prediction method according to claim 1, wherein the construction method of the dispute focus knowledge graph comprises the following steps:
s11, identifying and extracting object entities, attributes and the relationship among the object entities by using a natural language processing and text semantic analysis method;
s12, performing knowledge fusion on the identified and extracted object entities and attributes, and eliminating synonymous and wrong objects by combining and eliminating ambiguous concepts through entity connection and knowledge;
and S13, through knowledge fusion, giving definite definition to the concepts and the connection between the concepts in a formalized mode, and constructing a knowledge graph related to the current situation.
3. The case dispute focus-based auxiliary referee result prediction method of claim 1, wherein in step S1, the court trial model comprises: legal approval model, factual approval model, and claims automatic calculation model.
4. The case dispute focus-based auxiliary referee result prediction method according to claim 1, wherein in step S2, the concrete method for case preparation comprises:
s21, collecting the information in the case handling system, the dispute mediation system and the electronic file system;
s22, extracting and summarizing the dissimilarity points of the parties in the aspects of case facts and requirements through judging litigation requests of the two parties of the original quilt, and forming a court trial outline;
s23, pushing the complaint and evidence materials related to the dispute points to the judge, assisting the judge to understand the case, and completing the preparation of antecourt data.
5. The case dispute focus-based auxiliary referee result prediction method as claimed in claim 4, wherein in step S22, the method for extracting and inducing the dissimilarity of the parties in terms of case facts and complaints comprises:
s221, training and optimizing the model by data, understanding content semantics and identifying contradiction claims of both sides;
s222, adopting contradiction problem identification to refine and summarize contradictory advices of both sides of the dispute, and adopting dispute collection point classification accuracy and the credit acquisition rate of lawyers, judges, inspectors and other judicial workers to verify;
s223, the semantic analysis and the contradictory assertion detection effects are improved, the dispute focus is refined, and whether the legal documents are wrongly summarized or the dispute focus is omitted is checked.
6. The method for predicting the result of an auxiliary referee based on case dispute focus as claimed in claim 1, wherein said step S3 comprises the following steps:
s31, fusing linguistic features into word vectors representing learning by using a deep learning method, and establishing a bidirectional RNN neural network model;
s32, constructing a fine-grained opinion analysis model based on sequence labeling, and fusing word vectors, parts of speech and dependency relationship linguistic features of the text;
s33, learning modification and semantic information of the text, and constructing a time sequence labeling model;
and S34, extracting attribute entities to judge the emotion polarity of the text, analyzing and identifying the advices of both sides, and obtaining the refining information of the dispute focus.
7. An auxiliary judgment result prediction system based on case dispute focus comprises a data entry module, a pre-court preparation module, a court trial module and an output module, wherein,
the data entry module is used for constructing and storing a dispute focus knowledge graph and a court trial model;
a pre-court preparation module, which is used for inducing the appeal dissimilarity points of both parties based on the prior advertised litigation request according to the dispute focus knowledge graph, pushing the evidence related to the dispute points to the court and completing the pre-court preparation;
the court trial module is used for collecting confidence of disputed points and evidences of both parties according to the court trial debates of both parties of the parties based on a natural language processing technology, matching judgment results with factual statements according to a set court trial model and predicting case results;
and the output module is used for outputting the court trial documents.
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| CN110888943A (en) * | 2019-11-08 | 2020-03-17 | 太极计算机股份有限公司 | Method and system for auxiliary generation of court referee document based on micro-template |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114626826A (en) * | 2022-03-23 | 2022-06-14 | 德稻全球创新网络（北京）有限公司 | Cooperative project collaborative mobile communication system and method among enterprises |
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