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Dependency-position relation graph convolutional network with hierarchical attention mechanism for relation extraction

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Abstract

Existing research extensively incorporates syntactic information, especially dependency trees, to enhance the performance of relation extraction tasks. However, relying solely on dependency information may not fully exploit the rich semantic and syntactic information contained in sentences, and not all information in dependency trees is substantively helpful for relation extraction. Therefore, this paper proposes the Dependent-Position Relation Graph Convolutional network with Hierarchical Attention (DPR-GHA) for relation extraction, a method that integrates dependency relations and position relations into a hierarchical attention mechanism to effectively capture the relations between entities in text. The method aims to capture rich semantic information and enhance the performance of relation extraction. Specifically, we introduce the dependency relations of sentences and position relations of words to model global dependencies and local features, respectively. Subsequently, a novel hierarchical attention mechanism is introduced into the Graph Convolutional Network (GCN), dynamically adjusting the weights between nodes based on the input of the graph convolutional layer. This adaptive information aggregation enables each node to aggregate information adaptively according to its context and the importance of neighboring nodes. The research results on the SemEval-2010 Task 8 and KBP37 datasets thoroughly validate the effectiveness of the proposed model, demonstrating its significant performance advantage in relation extraction tasks.

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No datasets were generated or analyzed during the current study.

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Acknowledgements

This work is supported in part by Postgraduate Education Reform and Quality Improvement Project of Henan Province under Grant YJS2022JD26; in part by Research and Practice Project of Postgraduate Education and Teaching Reform of Henan University under Grant YJSJG2022XJ039; and in part by Postgraduate Training Innovation and Quality Improvement Action Plan Project of Henan University under Grant (Talent Plan) SYLYC2022148 and SYLYC2023138, Grant (Education Innovation Training Base) SYLJD2022008, Grant (Case Library) SYLAL2023017. (Corresponding authors: Ying Wang)

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Authors and Affiliations

  1. Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks, Henan University, Kaifeng, 475004, Henan, China

    Nan Li

  2. Software Engineering Intelligent Information Processing Innovation and Attracting Intelligence Base(CXJDB2020002) in Subject Innovation Base of Henan Higher Universities, Henan University, Kaifeng, 475004, Henan, China

    Ying Wang

  3. Henan Provincial Communications and Transportation Dispatch and Command Center, Zhengzhou, 450000, Henan, China

    Tianxu Liu

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  1. Nan Li
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  2. Ying Wang
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  3. Tianxu Liu
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Contributions

NL and YW made substantial contributions to the conception or design of the work. NL and YW conducted experiments and performed data analysis. NL, YW, and TL drafted the original manuscript. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Ying Wang.

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Li, N., Wang, Y. & Liu, T. Dependency-position relation graph convolutional network with hierarchical attention mechanism for relation extraction. J Supercomput 80, 18954–18976 (2024). https://doi.org/10.1007/s11227-024-06204-8

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