Scientific Information Research

Syntax-enhanced Boundary-aware Named Entity Recognition Model

Chuanming YU, School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073
Bin DENG, School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073
Zhengang ZHANG, School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073

Keywords

named entity recognition, BERT, boundary-aware, GAT, syntax enhancement

Abstract

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] Empirical studies are conducted on the ACE2005, MSRA, and People's Daily datasets, the F1 scores of the SynBNER model are respectively 86.11%, 96.03%, and 95.81%. The experimental results demonstrate that the method of enhancing entity boundary awareness with syntax information can significantly improve the effectiveness of named entity recognition.

Digital Object Identifier (DOI)

10.19809/j.cnki.kjqbyj.2025.03.002

Recommended Citation

YU, Chuanming; DENG, Bin; and ZHANG, Zhengang (2025) "Syntax-enhanced Boundary-aware Named Entity Recognition Model," Scientific Information Research: Vol. 7: Iss. 3, Article 2.
DOI: 10.19809/j.cnki.kjqbyj.2025.03.002
Available at: https://eng.kjqbyj.com/journal/vol7/iss3/2

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