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Employing Multi-granularity Features to Extract Entity Relation in Dialogue

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Natural Language Processing and Chinese Computing (NLPCC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13028))

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Abstract

Extracting relational triples from unstructured text is essential for the construction of large-scale knowledge graphs, QA and other downstream tasks. The purpose of dialogue relation extraction is to extract the relations between entities from the multi-person dialogue texts. The existing dialogue relation extraction models only focused on coarse-grained global information and ignored fine-grained local information. In this paper, we propose a dialogue relation extraction model BERT-MG to capture the features on different granularity at different BERT layers to take advantage of the fine-grained dialogue features. Moreover, we design a type-confidence mechanism to use the entity type information to assist relation inference. Experimental results on the DialogRE dataset prove that our proposed model BERT-MG outperforms the SOTA baselines.

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Acknowledgments

The authors would like to thank the three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (No. 61836007, 61772354 and 61773276), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Qiqi Wang & Peifeng Li

Authors
  1. Qiqi Wang
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  2. Peifeng Li
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Corresponding author

Correspondence to Peifeng Li .

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Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Lu Wang

  2. Peking University, Beijing, China

    Yansong Feng

  3. Soochow University, Suzhou, China

    Yu Hong

  4. Tianjin University, Tianjin, China

    Ruifang He

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Wang, Q., Li, P. (2021). Employing Multi-granularity Features to Extract Entity Relation in Dialogue. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-88480-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88479-6

  • Online ISBN: 978-3-030-88480-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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