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BIRL: Bidirectional-Interaction Reinforcement Learning Framework for Joint Relation and Entity Extraction

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Database Systems for Advanced Applications (DASFAA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12682))

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Abstract

Joint relation and entity extraction is a crucial technology to construct a knowledge graph. However, most existing methods (i) can not fully capture the beneficial connections between relation extraction and entity extraction tasks, and (ii) can not combat the noisy data in the training dataset. To overcome these problems, this paper proposes a novel Bidirectional-Interaction Reinforcement Learning (BIRL) framework, to extract entities and relations from plain text. Especially, we apply a relation calibration RL policy to (i) measure relation consistency and enhance the bidirectional interaction between entity mentions and relation types; and (ii) guide a dynamic selection strategy to remove noise from training dataset. Moreover, we also introduce a data augmentation module for bridging the gap of data-efficiency and generalization. Empirical studies on two real-world datasets confirm the superiority of the proposed model.

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Acknowledgement

We thank anonymous reviewers for valuable comments. This work is funded by: (i) the National Natural Science Foundation of China (No. U19B2026); (ii) the New Generation of Artificial Intelligence Special Action Project (No. AI20191125008); and (iii) the National Integrated Big Data Center Pilot Project (No. 20500908, 17111001,17111002).

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Authors and Affiliations

  1. National Engineering Laboratory for Risk Perception and Prevention (RPP), China Academy of Electronics and Information Technology of CETC, Beijing, 100041, China

    Yashen Wang & Huanhuan Zhang

Authors
  1. Yashen Wang
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  2. Huanhuan Zhang
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Corresponding author

Correspondence to Yashen Wang .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  4. The Pennsylvania State University, University Park, PA, USA

    Wang-Chien Lee

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  6. Athens University of Economics and Business, Athens, Greece

    Vana Kalogeraki

  7. National Cheng Kung University, Tainan City, Taiwan

    Jen-Wei Huang

  8. National Tsing Hua University, Hsinchu, Taiwan

    Chih-Ya Shen

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Wang, Y., Zhang, H. (2021). BIRL: Bidirectional-Interaction Reinforcement Learning Framework for Joint Relation and Entity Extraction. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12682. Springer, Cham. https://doi.org/10.1007/978-3-030-73197-7_32

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  • DOI: https://doi.org/10.1007/978-3-030-73197-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73196-0

  • Online ISBN: 978-3-030-73197-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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