|Table of Contents|

Chinese Named Entity Recognition Method Based on Ensemble Learning(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2022年03期
Page:
123-131
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Chinese Named Entity Recognition Method Based on Ensemble Learning
Author(s):
Liang Bingtao1; Ni Yunfeng2
(1.Hangzhou Youxing Technology CO.,LTD.,Zhejiang 310000,China)(2.College of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an 710600,China)
Keywords:
named entity recognition; BERT model; ensemble learning; attention mechanism; IDCNN
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2022.03.016
Abstract:
Aiming at the problems existing in the classical BiLSTM-CRF(bi-directional long short-term memory-conditional random field)model of Chinese named entity recognition,such as the inability of the embedding vector cannot represent polysemy,the attention of the coding layer is distracted and lack of capturing local spatial features. This paper proposes an ensemble model that combines the advantages of the BERT-BiGRU-MHA-CRF and BERT-IDCNN-CRF models to complete named entity recognition. This method uses the BERT model to obtain a semantic vector containing contextual information,and then inputs the semantic vector into BiGRU-MHA(bi-directional gated recurrent unit-multi head attention)and IDCNN(Iterated Dilated Convolutional Neural Network)networks. The former captures the timing characteristics of the input sequence and can assign weights according to the importance of the characters,the latter mainly captures the spatial characteristics of the input,and uses the mean ensemble method to fuse the captured features. Finally,the global optimal annotation sequence is obtained through the CRF layer. The F1 values of the ensemble model on the datasets of People's Daily and Microsoft Research Asia(MSRA)reached 96.09% and 95.01%,respectively. Compared with the single model,it has increased by more than 0.74% and 0.55%,respectively,which verifies the effectiveness of the method in this paper.

References:

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Last Update: 2022-09-15