Traditional named entity recognition methods need professional domain knowl-edge and a large amount of human participation to extract features,as well as the Chinese named entity recognition method based on a neural n...Traditional named entity recognition methods need professional domain knowl-edge and a large amount of human participation to extract features,as well as the Chinese named entity recognition method based on a neural network model,which brings the prob-lem that vector representation is too singular in the process of character vector representa-tion.To solve the above problem,we propose a Chinese named entity recognition method based on the BERT-BiLSTM-ATT-CRF model.Firstly,we use the bidirectional encoder representations from transformers(BERT)pre-training language model to obtain the se-mantic vector of the word according to the context information of the word;Secondly,the word vectors trained by BERT are input into the bidirectional long-term and short-term memory network embedded with attention mechanism(BiLSTM-ATT)to capture the most important semantic information in the sentence;Finally,the conditional random field(CRF)is used to learn the dependence between adjacent tags to obtain the global optimal sentence level tag sequence.The experimental results show that the proposed model achieves state-of-the-art performance on both Microsoft Research Asia(MSRA)corpus and people’s daily corpus,with F1 values of 94.77% and 95.97% respectively.展开更多
文摘Traditional named entity recognition methods need professional domain knowl-edge and a large amount of human participation to extract features,as well as the Chinese named entity recognition method based on a neural network model,which brings the prob-lem that vector representation is too singular in the process of character vector representa-tion.To solve the above problem,we propose a Chinese named entity recognition method based on the BERT-BiLSTM-ATT-CRF model.Firstly,we use the bidirectional encoder representations from transformers(BERT)pre-training language model to obtain the se-mantic vector of the word according to the context information of the word;Secondly,the word vectors trained by BERT are input into the bidirectional long-term and short-term memory network embedded with attention mechanism(BiLSTM-ATT)to capture the most important semantic information in the sentence;Finally,the conditional random field(CRF)is used to learn the dependence between adjacent tags to obtain the global optimal sentence level tag sequence.The experimental results show that the proposed model achieves state-of-the-art performance on both Microsoft Research Asia(MSRA)corpus and people’s daily corpus,with F1 values of 94.77% and 95.97% respectively.
