Word2Vec-BiLSTM-CRF-based named entity recognition method in the legal field

Abstract

The invention discloses a Word2Vec-BiLSTM-CRF-based named entity recognition method in the legal field, and the method specifically comprises the following steps: obtaining original data in the legal field, and carrying out the preprocessing of the data, so as to obtain training corpus data; inputting the obtained training corpus data into a Word2Vec algorithm and combining the Word2Vec algorithm with a CBOW model so as to obtain word vectors aiming at the legal field; labeling the training corpus data obtained through preprocessing in combination with template matching and Chinese corpus pause and other modes, obtaining labeled corpus, serving Bi-LSTM as a coding layer of a model, and combining the obtained labeled corpus and obtained word vectors to serve as input and output of the coding layer to obtain text semantic information features; and taking the text semantic information features obtained by the Bi-LSTM layer as input of the CRF, and finally outputting an identification result of the named entity. The method has the advantages that rich entities in the legal document can be identified, fine-grained description of the entities in the legal field and data structuring in the legal field are realized, and the method is of great significance in further mining the relationship among different entities in the legal field.

Description

technical field [0001] The invention relates to the field of named entity recognition, in particular to an entity extraction method in the legal field based on the Word2Vec-BiLSTM-CRF model. Background technique [0002] In the field of law, whether in the investigation process of a case or in court proceedings, there are many types and complex named entities involved. The most common of these entities are the elements of the case, such as people (suspects, victims), time, place, motivation, events, etc. These different case elements have different characteristics and manifestations in different contexts of criminal charges. [0003] There are many kinds of entities in the legal domain, and the representation forms of these entities are different. It is of great significance to use a unified method to identify these named entities with different representation forms, realize the fine-grained description of entities in the legal field, structure the data in the legal field,...

AI Technical Summary

Problems solved by technology

[0005] Since the named entities of patents are relatively simple and unified in the legal field, this method can realize the extraction of patent terms, but the effect of this extraction method cannot be applied to the legal field with complex named entities, and there is no effective identification method to mine entities in the legal field , the extraction effect is poor

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