Open AccessAn End-to-End Named Entity Recognition Model for Chinese
Author(s) -
Cheng Gong,
Jiuyang Tang,
Zhen Li
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/692/1/012050
Subject(s) - computer science , end to end principle , named entity recognition , artificial intelligence , sentence , task (project management) , natural language processing , key (lock) , layer (electronics) , artificial neural network , chemistry , computer security , management , organic chemistry , economics
Named entity recognition is an important basic task in natural language processing. This paper proposes a named entity recognition method for end-to-end and efficient deep-loop neural networks. Using BERT to train sub-vectors as raw input enables the model to obtain more comprehensive text information, and at the same time, the BiLSTM network is focused on the attention mechanism, so that the network pays more attention to the key information in the text and ignores the redundant information to improve the recognition efficiency of the model. Finally, the relationship between any two tags is captured by the CRF layer, and the entire sentence is decoded and predicted. Experiments show that the method performs well on MSRA corpus.