End-to-End Chinese Entity Recognition Based on BERT-BiLSTM-ATT-CRF

• 作者: LI Daiyi TU Yaofeng ZHOU Xiangsheng ZHANG Yangming MA Zongmin
• 来源期刊: 中兴通讯技术（英文版）
• 年，卷（期）: 2022,（z1）
• 所属期刊栏目: Research Paper
• 页码范围: 27-35
• 页数: 9页
• 语种: 英文
• DOI: 10.12142/ZTECOM.2022S1005

摘要

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.