A Chinese named entity recognition model based on multi-feature fusion embedding#br#

Computer Engineering & Science ›› 2024, Vol. 46 ›› Issue (8): 1473-1481.

• Artificial Intelligence and Data Mining • Previous Articles Next Articles

A Chinese named entity recognition model based on multi-feature fusion embedding#br#

LIU Xiao-hua1，XU Ru-zhi1，YANG Cheng-yue2#br#

(1.School of Control and Computer Engineering,North China Electric Power University,Beijing 102206;
2.Big Data Center of State Grid Corporation of China,Beijing 100052,China)

Received:2023-03-31 Revised:2023-06-14 Online:2024-08-25 Published:2024-09-02

Abstract

Abstract:

In order to solve the problems of differences in Chinese glyphs and blurred boundaries of Chinese words, a Chinese named entity recognition model based on multi-feature fusion embedding is proposed. On the basis of extracting semantic features, glyph features are captured based on convolutional neural network and multi-headed self-attention mechanism, word features are obtained with reference to the word vector embedding table, and the bidirectional long short-term memory neural network is used to learn the context representation of long distance. Finally the constraint conditions in sentence sequence labels are learned by combining the conditional random field to realize Chinese named entity recognition. The F1 values on the Resume, Weibo and People Daily datasets reach 96.66%, 70.84% and 96.15%, respectively, which proves that the proposed model effectively improves the performance of Chinese named entity recognition tasks.

Key words: named entity recognition, feature fusion, multi-headed self-attention mechanism

LIU Xiao-hua, XU Ru-zhi, YANG Cheng-yue. A Chinese named entity recognition model based on multi-feature fusion embedding#br#[J]. Computer Engineering & Science, 2024, 46(8): 1473-1481.

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A Chinese named entity recognition model based on multi-feature fusion embedding#br#

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