Chinese relation extraction based on dynamic dependency driving and multiple feature enhancement.
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| Title: | Chinese relation extraction based on dynamic dependency driving and multiple feature enhancement. |
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| Authors: | HUANG, Mingwei1 huangmw_ly@163.com, HAN, Hu1,2 hanhu_lzjtu@mail.lzjtu.cn, XU, Xuefeng1 xuefeng_1998@163.com, WANG, Tingting1 wangtt_ly@163.com |
| Source: | Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Feb2026, Vol. 48 Issue 2, p319-329. 11p. |
| Subjects: | Natural language processing, Graph neural networks, Semantics (Philosophy), Syntax (Grammar) |
| Abstract: | As a subtask in the field of natural language processing (NLP), relation extraction aims to identify the relationships between specific entity pairs from unstructured text. Aiming at the problems of incomplete extraction of key semantic features and the introduction of syntactic knowledge accompanied by a large amount of noise information in existing studies on Chinese relation extraction, a dynamic dependency-driven and multiple feature-enhanced Chinese relation extraction model is constructed. The model consists of two channels. In channel one, the original dependency parse trees for entity pairs are reconstructed and dynamically pruned to remove redundant syntactic dependencies, and deep syntactic features are captured through a graph convolutional network (GCN). In channel two, relative position vectors are constructed for entities, and segmented feature extraction is performed on these vectors using segmented convolution to obtain local semantic features. Global semantic features are captured using a hybrid attention mechanism, and local and global semantic features are fused through a gating mechanism. Finally, the feature representations from the two channels are interactively fused. Experimental results demonstrate that the model outperforms baseline models on four public datasets: COAE2016, SanWen, FinRE, and SciRE. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Chinese relation extraction based on dynamic dependency driving and multiple feature enhancement. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22HUANG%2C+Mingwei%22">HUANG, Mingwei</searchLink><relatesTo>1</relatesTo><i> huangmw_ly@163.com</i><br /><searchLink fieldCode="AR" term="%22HAN%2C+Hu%22">HAN, Hu</searchLink><relatesTo>1,2</relatesTo><i> hanhu_lzjtu@mail.lzjtu.cn</i><br /><searchLink fieldCode="AR" term="%22XU%2C+Xuefeng%22">XU, Xuefeng</searchLink><relatesTo>1</relatesTo><i> xuefeng_1998@163.com</i><br /><searchLink fieldCode="AR" term="%22WANG%2C+Tingting%22">WANG, Tingting</searchLink><relatesTo>1</relatesTo><i> wangtt_ly@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Engineering+%26+Science+%2F+Jisuanji+Gongcheng+yu+Kexue%22">Computer Engineering & Science / Jisuanji Gongcheng yu Kexue</searchLink>. Feb2026, Vol. 48 Issue 2, p319-329. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics+%28Philosophy%29%22">Semantics (Philosophy)</searchLink><br /><searchLink fieldCode="DE" term="%22Syntax+%28Grammar%29%22">Syntax (Grammar)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As a subtask in the field of natural language processing (NLP), relation extraction aims to identify the relationships between specific entity pairs from unstructured text. Aiming at the problems of incomplete extraction of key semantic features and the introduction of syntactic knowledge accompanied by a large amount of noise information in existing studies on Chinese relation extraction, a dynamic dependency-driven and multiple feature-enhanced Chinese relation extraction model is constructed. The model consists of two channels. In channel one, the original dependency parse trees for entity pairs are reconstructed and dynamically pruned to remove redundant syntactic dependencies, and deep syntactic features are captured through a graph convolutional network (GCN). In channel two, relative position vectors are constructed for entities, and segmented feature extraction is performed on these vectors using segmented convolution to obtain local semantic features. Global semantic features are captured using a hybrid attention mechanism, and local and global semantic features are fused through a gating mechanism. Finally, the feature representations from the two channels are interactively fused. Experimental results demonstrate that the model outperforms baseline models on four public datasets: COAE2016, SanWen, FinRE, and SciRE. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3969/j.issn.1007-130X.2026.02.013 Languages: – Code: chi Text: Chinese PhysicalDescription: Pagination: PageCount: 11 StartPage: 319 Subjects: – SubjectFull: Natural language processing Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Semantics (Philosophy) Type: general – SubjectFull: Syntax (Grammar) Type: general Titles: – TitleFull: Chinese relation extraction based on dynamic dependency driving and multiple feature enhancement. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: HUANG, Mingwei – PersonEntity: Name: NameFull: HAN, Hu – PersonEntity: Name: NameFull: XU, Xuefeng – PersonEntity: Name: NameFull: WANG, Tingting IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1007130X Numbering: – Type: volume Value: 48 – Type: issue Value: 2 Titles: – TitleFull: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue Type: main |
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