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

doi:10.12142/ZTECOM.2022S1005

摘要/Abstract

Abstract:

Traditional named entity recognition methods need professional domain knowledge 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 problem that vector representation is too singular in the process of character vector representation. 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 semantic 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.

Key words: named entity recognition (NER), feature extraction, BERT model, BiLSTM, attention mechanism, CRF

. [J]. ZTE Communications, 2022, 20(S1): 27-35.

LI Daiyi, TU Yaofeng, ZHOU Xiangsheng, ZHANG Yangming, MA Zongmin. End-to-End Chinese Entity Recognition Based on BERT-BiLSTM-ATT-CRF[J]. ZTE Communications, 2022, 20(S1): 27-35.

图/表 9

参考文献 38

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Dataset	Type	Train	Dev	Test
People’s Daily	Sentence	17.6k	0.9k	1.7k
MSRA	Sentence	46.4k	Null	4.4k

Dataset	Type	Train	Dev	Test
People’s Daily	Sentence	17.6k	0.9k	1.7k
MSRA	Sentence	46.4k	Null	4.4k

Layer	Parameter	Value
BERT	Transformer layer number	12
	Hidden layer dimension	768
	Head number	12
BiLSTM	Optimizer	Adam
	Batch size	32
	Dropout rate	0.5
	Learning rate	0.001 5
	Hidden layer number	200

Layer	Parameter	Value
BERT	Transformer layer number	12
	Hidden layer dimension	768
	Head number	12
BiLSTM	Optimizer	Adam
	Batch size	32
	Dropout rate	0.5
	Learning rate	0.001 5
	Hidden layer number	200

Model	P/%	R/%	F1/%
LSTM-CRF	84.20	80.20	82.00
BiLSTM	81.08	79.21	80.05
BiLSTM-CRF	87.21	83.21	85.09
BERT-BiLSTM-CRF	96.04	95.30	95.67
BERT-BiLSTM-ATT-CRF	96.28	95.67	95.97