CN111414454B - Law recommendation processing method based on bert model and law knowledge - Google Patents

Law recommendation processing method based on bert model and law knowledge Download PDF

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CN111414454B
CN111414454B CN202010180118.2A CN202010180118A CN111414454B CN 111414454 B CN111414454 B CN 111414454B CN 202010180118 A CN202010180118 A CN 202010180118A CN 111414454 B CN111414454 B CN 111414454B
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knowledge
case description
semantic representation
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representation vector
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CN111414454A (en
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余正涛
唐光远
张亚飞
郭军军
高盛祥
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Kunming University of Science and Technology
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Abstract

The invention relates to a law provision recommendation processing method based on a bert model and law provision knowledge, and belongs to the technical field of data processing. The method extracts keywords from law knowledge in the judicial field; semantic representation is carried out on case description texts and legal knowledge keywords; based on an attention mechanism, fusing the case description text semantic representation vector and the legal knowledge keyword semantic representation vector to obtain a case description feature vector fused with the legal knowledge keywords; and performing linear transformation and softmax on case description feature vectors fused with the French knowledge keywords, and finally realizing French recommendation. The invention fuses the legal provision knowledge and case description to realize intelligent legal provision recommendation based on knowledge driving.

Description

Law recommendation processing method based on bert model and law knowledge
Technical Field
The invention relates to a law enforcement recommendation processing method based on a bert model and law enforcement knowledge, and belongs to the technical field of data processing.
Background
The legal provision recommendation based on the association of the case description content and the legal provision is to realize the selection and recommendation of related legal provisions according to the description content of the case and by combining legal provision knowledge in the judicial field.
The existing law statement recommendation algorithms are mostly based on a data-driven mode, early law judgment and clause prediction are realized based on a statistical method, meanwhile, with the continuous and deep development of a machine learning algorithm, the law statement recommendation is realized based on a text classification mode, for example, an SVM (support vector machine) method is used for predicting judgment data results, and a preliminary law statement classification method can also be used. In recent years, with the development of deep learning, a method based on a deep neural network has been developed greatly in the field of legal decision prediction.
The law recommendation is not a simple law prediction process based on data driving, and has strong judicial field characteristics, in the case approval process, a judge usually analyzes cases by taking legal knowledge as a criterion, and selects corresponding law knowledge as the basis for judging criminal states and names of crimes, for example, in the case approval process of criminal cases such as 'robbery' and 'robbery', 'fraud' and 'extortion', the judge usually pays attention to the following vocabularies in the process of adjudication, as shown in table 1.
TABLE 1 analysis of jurisdictional concerns in judge review
Figure BDA0002412218260000011
Therefore, in case trial, the judge usually needs to repeatedly re-interpret information such as case description based on the core words of the law. Because the judicial officer cannot automatically recommend proper legal rules to the officer in the case reviewing process, the burden of the officer on the case reviewing is increased, and the judicial judging efficiency is also reduced. Therefore, the invention provides a law provision recommendation processing method based on a bert model and law provision knowledge.
Disclosure of Invention
The invention provides a law enforcement recommendation processing method based on a bert model and law enforcement knowledge, which fuses the law enforcement knowledge and case description to realize intelligent law enforcement recommendation based on knowledge driving.
The technical scheme of the invention is as follows:
extracting key words from judicial domain law knowledge;
semantic representation is carried out on the case description text and the legal knowledge key words;
based on an attention mechanism, fusing the case description text semantic representation vector and the legal knowledge keyword semantic representation vector to obtain a case description feature vector fused with the legal knowledge keywords;
and performing linear transformation and softmax on case description feature vectors fused with the French knowledge keywords, and finally realizing French recommendation.
And further, extracting keywords from the French knowledge in the judicial field by using a TextRank-based method to obtain the keywords of the French knowledge.
Further, comprising:
semantic representation is carried out on the case description text by adopting a bert-based model and a bidirectional LSTM in sequence to obtain a case description text semantic representation vector;
semantic representation is carried out on the French knowledge key words by adopting a bert-based model and a bidirectional LSTM in sequence, and a French knowledge key word semantic representation vector is obtained.
Further, performing semantic representation on the case description text and the legal knowledge keywords comprises:
the case description text X is [ X1, …, xN ] and the French knowledge keyword text set Y is [ Y1, …, ym ], wherein N represents the length of the case description text, m is the length of the French knowledge keyword text set, a bert pre-training model is adopted to respectively represent the case description text, and based on the bert model, the semantic representation vectors of specific text descriptions are respectively obtained;
FX＝BERT(X)
FY＝BERT(Y)
wherein, FXAnd FYRespectively representing a bert-based case description text semantic representation vector and a legal knowledge keyword semantic representation vector.
Meanwhile, in order to improve the continuous representation capability of the text sequence information, a bidirectional LSTM layer is added behind the bert module, so that the context feature information capability of the text feature vector is further improved, specifically:
FX1＝BiLSTM(FX)
FY1＝BiLSTM(FY)
wherein, FX1And FY1And respectively representing the final case description text semantic representation vector and the legal knowledge keyword semantic representation vector.
Further, the obtaining of case description feature vectors fused with the legal knowledge keywords comprises:
semantic representation vector F for case description textXHem knowledge keyword semantic representation vector FYPerforming attention calculation to obtain a case description text semantic representation vector FXHem knowledge keyword semantic representation vector FYFused feature attention;
semantic representation vector F of case description textXHem knowledge keyword semantic representation vector FYThe fused feature attention is normalized, and finally, a case description text semantic representation vector F is expressedXAnd carrying out weighted summation based on the attention weight to obtain case description feature vectors fused with the keywords of the law knowledge.
The method specifically comprises the following steps:
step 4.1, based on the attention mechanism, semantic representation vector F of case description textXHem knowledge keyword semantic representation vector FYPerforming attention calculation; case description text semantic representation vector FXHend-law knowledge keyword semantic representation vector FYThe fused attention calculation formula is as follows:
f(FX(i),FY)＝FX(i)*FY T
wherein, FX(i) A semantic representation vector of the ith text representing the case description text;
step 4.2, semantic representation vector F of case description textXHem knowledge keyword semantic representation vector FYThe concrete formula for normalizing the fused feature attention is as follows:
ai＝softmax[f(FX(i),FY)]
wherein, aiExpressing attention vectors of the French knowledge key words and the ith text of the case description text;
step 4.3, representing the semantic representation vector F of the case description textXCarrying out weighted summation based on attention weight to obtain case description feature vector F fused with law knowledge keywordsXYThe concrete formula is as follows,
FXY＝∑aiFX(i)。
the invention has the beneficial effects that: the method combines a Bert model with strong text representation and text comprehension capability, refers the model to a law article recommendation task and carries out deduction, and a better effect is achieved; the invention adopts the bert model to realize accurate representation of legal provision knowledge and case description, and improves the effect of legal provision recommendation.
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FIG. 1 is a diagram of the architecture of the Law recommendation based on the fusion of the bert model and the Law knowledge in the present invention.
Detailed Description
Example 1: as shown in fig. 1, the technical solution of the invention is that, in the law recommendation processing method based on the bert model and law knowledge:
step 1, acquiring a case description text; selecting a training set and a testing set from the training sets;
the invention uses a usage research cup public data set to obtain case description texts, and selects a training set of 60 ten thousand case description texts and a test set of 10 ten thousand case description texts;
in the invention, for example, when a clockwork spring of a criminal case is recommended, the case belongs to the criminal case, and the criminal law knowledge in the judicial field is correspondingly adopted to construct a database;
step 2, extracting keywords from judicial domain law knowledge;
step 3, performing semantic representation on case description texts and legal knowledge keywords;
step 4, fusing the case description text semantic representation vector and the legal knowledge keyword semantic representation vector based on an attention mechanism to obtain a case description feature vector fused with the legal knowledge keywords;
and 5, performing linear transformation and softmax on case description feature vectors fused with the French knowledge keywords, and finally realizing French recommendation.
And further, extracting keywords from the French knowledge in the judicial field by using a TextRank-based method to obtain the keywords of the French knowledge.
Further, comprising:
semantic representation is carried out on the case description text by adopting a bert-based model and a bidirectional LSTM in sequence to obtain a case description text semantic representation vector;
semantic representation is carried out on the French knowledge key words by adopting a bert-based model and a bidirectional LSTM in sequence, and a French knowledge key word semantic representation vector is obtained.
Further, performing semantic representation on the case description text and the legal knowledge keywords comprises:
the case description text X is [ X1, …, xN ] and the French knowledge keyword text set Y is [ Y1, …, ym ], wherein N represents the length of the case description text, m is the length of the French knowledge keyword text set, a bert pre-training model is adopted to respectively represent the case description text, and based on the bert model, the semantic representation vectors of specific text descriptions are respectively obtained;
FX＝BERT(X)
FY＝BERT(Y)
wherein, FXAnd FYRespectively representing a bert-based case description text semantic representation vector and a legal knowledge keyword semantic representation vector.
Meanwhile, in order to improve the continuous representation capability of the text sequence information, a bidirectional LSTM layer is added behind the bert module, so that the context feature information capability of the text feature vector is further improved, specifically:
FX1＝BiLSTM(FX)
FY1＝BiLSTM(FY)
wherein, FX1And FY1Respectively representing final case description text semantic representation vector and legal knowledgeAnd identifying a keyword semantic representation vector.
Further, the step 4 comprises:
semantic representation vector F for case description textXHem knowledge keyword semantic representation vector FYPerforming attention calculation to obtain a case description text semantic representation vector FXHem knowledge keyword semantic representation vector FYFused feature attention;
semantic representation vector F of case description textXHem knowledge keyword semantic representation vector FYThe fused feature attention is normalized, and finally, a case description text semantic representation vector F is expressedXAnd carrying out weighted summation based on the attention weight to obtain case description feature vectors fused with the keywords of the law knowledge.
The method specifically comprises the following steps:
step 4.1, based on the attention mechanism, semantic representation vector F of case description textXHend-law knowledge keyword semantic representation vector FYPerforming attention calculation; case description text semantic representation vector FXHem knowledge keyword semantic representation vector FYThe fused attention calculation formula is as follows:
f(FX(i),FY)＝FX(i)*FY T
wherein, FX(i) A semantic representation vector of the ith text representing the case description text;
step 4.2, semantic representation vector F of case description textXHem knowledge keyword semantic representation vector FYThe concrete formula for normalizing the fused feature attention is as follows:
ai＝softmax[f(FX(i),FY)]
wherein, aiExpressing the attention vector of the ith text of the French knowledge key word and case description text;
step 4.3, representing the semantic representation vector F of the case description textXCarrying out weighted summation based on attention weight to obtain case description characteristics fused with law knowledge keywordsVector FXYThe concrete formula is as follows,
FXY＝∑aiFX(i)
to more intuitively illustrate the process of law-recommended practice, a relevant example analysis about fraud is given, such as case fact description fact: aiming at a certain fraud case, the description of the case is included; legal provisions for fraud; fraud and guilt: the behavior of deceiving public and private properties with large amount by using fictional facts or a method of hiding true figures with illegal purposes.
Aiming at the case description, the keyword of the case description such as 'fraud public and private property' and 'large amount' of the fraud law is fused with the case description based on the attention mechanism, so that the attention weight of the case description text with the law knowledge can be pertinently improved, the targeted feature extraction is further realized, and the accurate case recommendation is finally realized.
To illustrate the effect of the present invention, the present invention is compared with the conventional law recommended method, as shown in tables 2, 3, and 4:
TABLE 2 comparison of the patented method with the conventional law enforcement recommendation method
Figure BDA0002412218260000061
Table 3: the invention relates to a law statement knowledge keyword ablation contrast experiment result
Data of Model (model) F1
Test set of French grinding cup 2018 Method for fusing law knowledge 0.92
Test set of French grinding cup 2018 Method for not fusing law knowledge 0.88
TABLE 4 comparative experiment of the bert model of the present invention and the Word2vec model
Data of Model (model) F1
Test set of French grinding cup 2018 Word2vec 0.89
Test set of French grinding cup 2018 Bert 0.92
According to the experimental result, model training is carried out based on the same training set, the accuracy (P), recall rate (R) and F1 values (F1) of the law bar recommendation of the proposed method are obviously superior to those of the traditional law bar recommendation method based on data driving on the given test set, and the proposed law bar recommendation method based on knowledge driving has certain effect improvement relative to a reference model. Different from the traditional data-driven-based method, the law knowledge keywords and the method for representing based on the bert model are integrated, so that the effect of the law recommendation model is better improved.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (2)

1. The law provision recommendation processing method based on the bert model and law provision knowledge is characterized by comprising the following steps of: the method comprises the following steps:
extracting key words from judicial domain law knowledge;
semantic representation is carried out on case description texts and legal knowledge keywords;
based on an attention mechanism, fusing the case description text semantic representation vector and the legal knowledge keyword semantic representation vector to obtain a case description feature vector fused with the legal knowledge keywords;
performing linear transformation and softmax on case description feature vectors fused with the French knowledge keywords, and finally realizing French recommendation;
the method comprises the following steps:
semantic representation is carried out on the case description text by sequentially adopting a bert-based model and a bidirectional LSTM to obtain a case description text semantic representation vector;
semantic representation is carried out on the French knowledge key words by adopting a bert-based model and a bidirectional LSTM in sequence to obtain semantic representation vectors of the French knowledge key words;
the semantic representation of the case description text and the legal knowledge keywords comprises the following steps:
the case description text X is [ X1, ·, xN ] and the French knowledge keyword text set Y is [ Y1,. and.ym ], wherein N represents the length of the case description text, m is the length of the French knowledge keyword text set, a bert pre-training model is adopted to represent the cases description text, specific semantic representation vectors of the text description are obtained respectively based on the bert model, and in order to improve the continuous representation capability of text sequence information, a bidirectional LSTM layer is added behind the bert module, so that the context feature information capability of the text feature vectors is further improved;
the obtaining of case description feature vectors fusing with the legal knowledge keywords comprises the following steps:
semantic representation vector F for case description textXHem knowledge keyword semantic representation vector FYPerforming attention calculation to obtain a case description text semantic representation vector FXHend-law knowledge keyword semantic representation vector FYFused feature attention;
semantic representation vector F of case description textXHem knowledge keyword semantic representation vector FYThe integrated feature attention of the case description text is normalized, and finally, a case description text semantic representation vector F is expressedXCarrying out weighted summation based on the attention weight to obtain case description feature vectors fused with the legal knowledge keywords;
the specific steps for obtaining the case description feature vector fused with the legal knowledge keywords are as follows:
step 4.1, based on the attention mechanism, semantic representation vector F of case description textXHem knowledge keyword semantic representation vector FYPerforming attention calculation; case description text semantic representation vector FXHem knowledge keyword semantic representation vector FYThe fused attention calculation formula is as follows:
f(FX(i)，FY)＝FX(i)*FY T
wherein, FX(i) A semantic representation vector of the ith text representing the case description text;
step 4.2, semantic representation vector F of case description textXHem knowledge keyword semantic representation vector FYThe concrete formula for normalizing the fused feature attention is as follows:
ai＝softmax[f(FX(i)，FY)]
wherein, aiExpressing attention vectors of the French knowledge key words and the ith text of the case description text;
step 4.3, representing the case description text semantic directionQuantity FXCarrying out weighted summation based on attention weight to obtain case description feature vector F fused with law knowledge keywordsXYThe specific formula is as follows,
FXY＝∑aiFX(i)。
2. the judicial recommendation processing method based on bert model and judicial knowledge according to claim 1, characterized in that the TextRank-based method is used for extracting keywords from judicial domain judicial knowledge to obtain the keywords of the judicial knowledge.
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