Open AccessAttention-BLSTM-CRF Based Method for Named Entity Recognition in Judicial Domain
Author(s) -
Chen Wang,
Bo Li,
Wenjing Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1616/1/012108
Subject(s) - computer science , named entity recognition , context (archaeology) , identification (biology) , field (mathematics) , natural language processing , set (abstract data type) , sequence (biology) , artificial intelligence , entity linking , decoding methods , test set , expression (computer science) , speech recognition , task (project management) , algorithm , programming language , paleontology , botany , genetics , mathematics , management , pure mathematics , economics , biology , knowledge base
Although the texts in the judicial field are relatively standardized, the entity categories are rich and the structure is different, and the entity expression is special in some legal documents, electronic files and guiding cases. In order to improve the effect of named entity recognition in the field of justice, this paper entity type can be divided into four categories, and presents a model of named entity recognition based on attention mechanism, structure improvement of input vector fusion CNN embed mode, BLSTM neural network at the same time we constantly study the characteristics of the context, finally by CRF decoding output sequence. The experimental results show that the method is helpful to the application research in the judicial field, and the experiment on the self-built corpus test set has achieved a good entity identification effect.