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A Learnable Graph Convolutional Neural Network Model for Relation Extraction

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Information Retrieval (CCIR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13819))

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Abstract

Relation extraction is the task of extracting the semantic relationships between two named entities in a sentence. The task relies on semantic dependencies relevant to named entities. Recently, graph convolutional neural networks have shown great potential in supporting this task, wherein dependency trees are usually adopted to learn semantic dependencies between entities. However, the requirement of external toolkits to parse sentences poses a problem, owing to them being error prone. Furthermore, entity relations and parsing structures vary in semantic expressions. Therefore, manually designed rules are required to prune the structure of the dependency trees. This study proposed a novel learnable graph convolutional neural network model (L-GCN) that directly encodes every word of a sentence as nodes of a graph neural network. Then, the L-GCN uses a learnable adjacency matrix to encode dependencies between nodes. The model offers the advantage of automatically learning high-order abstract representations of the semantic dependencies between words. Moreover, a fusion module was designed to aggregate the global and local semantic structure information of sentences. Further, the proposed L-GCN was evaluated on the ACE 2005 English dataset and Chinese Literature Text Corpus. The experimental results confirmed the effectiveness of L-GCN in learning the semantic dependencies of a relation instance. Moreover, it clearly outperformed previous dependency-tree-based models.

J. Xu, Y. Chen, Y. Qin and R. Huang—These authors contributed equally to this work.

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Authors and Affiliations

  1. State Key Laboratory of Public Big Data, College of Computer Science and Technology Guizhou University, Guiyang, 550025, China

    Jinling Xu, Yanping Chen, Yongbin Qin & Ruizhang Huang

Authors
  1. Jinling Xu
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  2. Yanping Chen
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  3. Yongbin Qin
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  4. Ruizhang Huang
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Correspondence to Yanping Chen .

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Editors and Affiliations

  1. Dingxin Building C403, Jilin University, Changchun, China

    Yi Chang

  2. College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China

    Xiaofei Zhu

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Xu, J., Chen, Y., Qin, Y., Huang, R. (2023). A Learnable Graph Convolutional Neural Network Model for Relation Extraction. In: Chang, Y., Zhu, X. (eds) Information Retrieval. CCIR 2022. Lecture Notes in Computer Science, vol 13819. Springer, Cham. https://doi.org/10.1007/978-3-031-24755-2_8

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  • DOI: https://doi.org/10.1007/978-3-031-24755-2_8

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