Chinese Named Entity Recognition Method Based on Bidirectional LSTMCNNCRF

LIU Yu-peng; LI Dong-dong

doi:10.15938/j.jhust.2020.01.017

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Chinese Named Entity Recognition Method Based on Bidirectional LSTMCNNCRF

更新时间：2026-08-31

- Chinese Named Entity Recognition Method Based on Bidirectional LSTMCNNCRF
- Vol. 25, Issue 1, Pages: 115-120(2020)
- 作者机构：
  
  哈尔滨理工大学软件与微电子学院,哈尔滨,150080
- 作者简介：
- 基金信息：
- DOI：10.15938/j.jhust.2020.01.017
  CLC： TP391.1
- Published：2020
- 稿件说明：
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LIU Yu-peng, LI Dong-dong. Chinese Named Entity Recognition Method Based on Bidirectional LSTMCNNCRF[J]. 2020, 25(1): 115-120.
DOI：

LIU Yu-peng, LI Dong-dong. Chinese Named Entity Recognition Method Based on Bidirectional LSTMCNNCRF[J]. 2020, 25(1): 115-120. DOI： 10.15938/j.jhust.2020.01.017.

摘要

摘要:传统的命名实体识别方法依赖大量的人工选择的特征和专业领域的外部知识，针对这一问题，提出了一种新颖的神经网络结构，该算法结合了双向LSTM，CNN和CRF可以同时自动获取到基于字符级别和词语级别的表示，是一种真正意义上的端到端的结构，不再需要人工选择特征和数据的预处理，可以应用到各个领域的命名实体识别任务中去。最后，通过实验证明该算法在医疗领域和新闻领域的F1值分别达到了90.97%和92.19%。

Abstract

Traditional algorithms of named entity recognition rely on a largenumber of artificial features and external knowledge To solve this problem

wepropose a novel neural network structure which combines the bidirectionalLSTM(Long ShortTerm Memory)

CNN(Convolution Neural Network) andCRF(Conditional Random Field) This algorithm can also automatically get bothcharacterlevel and wordlevel representations It is a truly endtoend system

requiring no feature engineering and data processing Thus

it can be easilyapplied to various fields of named entity recognition task Finally

theexperimental results show that the F1 measure of the algorithm in the medicaland news field is 9097% and 9219%

respectively

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Related Institution

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