Scientific Information Research
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.
First Page
12
Last Page
23
Submission Date
October 2024
Revision Date
November 2024
Acceptance Date
November 2024
Publication Date
July 2025
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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