基于改进的Transformer编码器的中文命名实体识别

郑洪浩; 于洪涛; 李邵梅

doi:10.11959/j.issn.2096-109x.2021041

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基于改进的Transformer编码器的中文命名实体识别

专栏Ⅱ：机器学习及安全应用 | 更新时间：2024-06-03

- 基于改进的Transformer编码器的中文命名实体识别
- Chinese NER based on improved Transformer encoder
- 网络与信息安全学报 2021年7卷第5期页码：105-112
- 作者机构：
- 作者简介：
  
  [ "郑洪浩（1992− ），男，山东济宁人，信息工程大学硕士生，主要研究方向为命名实体识别、关系抽取" ]
  [ "于洪涛（1970− ），男，辽宁丹东人，博士，信息工程大学研究员，主要研究方向为大数据与人工智能" ]
  [ "李邵梅（1982− ），女，湖北钟祥人，博士，信息工程大学副研究员，主要研究方向为计算机视觉" ]
- 基金信息：
  
  国家自然基金青年基金;The National Natural Science Foundation of China(62002384);国家重点研发计划;The National Key R＆D Program of China(2016QY03D0502);郑州市协同创新重大专项;Major Collaborative Innovation Projects of Zhengzhou(162/32410218)
- DOI：10.11959/j.issn.2096-109x.2021041
  中图分类号： TP391
- 网络首发：2021-10，
  
  纸质出版：2021-10-15
- 稿件说明：
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郑洪浩, 于洪涛, 李邵梅. 基于改进的Transformer编码器的中文命名实体识别[J]. 网络与信息安全学报, 2021,7(5):105-112.

Honghao ZHENG, Hongtao YU, Shaomei LI. Chinese NER based on improved Transformer encoder[J]. Chinese Journal of Network and Information Security, 2021, 7(5): 105-112.
郑洪浩, 于洪涛, 李邵梅. 基于改进的Transformer编码器的中文命名实体识别[J]. 网络与信息安全学报, 2021,7(5):105-112. DOI： 10.11959/j.issn.2096-109x.2021041.

Honghao ZHENG, Hongtao YU, Shaomei LI. Chinese NER based on improved Transformer encoder[J]. Chinese Journal of Network and Information Security, 2021, 7(5): 105-112. DOI： 10.11959/j.issn.2096-109x.2021041.

摘要

为了提高中文命名实体识别的效果，提出了基于 XLNET-Transformer_P-CRF 模型的方法，该方法使用了 Transformer_P 编码器，改进了传统 Transformer 编码器不能获取相对位置信息的缺点。实验结果表明，XLNET-Transformer_P-CRF模型在MSRA、OntoNotes4.0、Resume、微博数据集4类数据集上分别达到95.11%、80.54%、96.70%、71.46%的F1值，均高于中文命名实体识别的主流模型。

Abstract

In order to improve the effect of chinese named entity recognition

a method based on the XLNETTransformer_P-CRF model was proposed

which used the Transformer_P encoder

improved the shortcomings of the traditional Transformer encoder that couldn’t obtain relative position information.Experiments show that the XLNET-Transformer_P-CRF model achieves 95.11%

80.54%

96.70%

and 71.46% F1 values on the four types of data sets: MSRA

OntoNotes4.0

Resume

and Weibo

which are all higher than other mainstream chinese NER model.

关键词

Keywords

references

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