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International Conference on Web Information Systems Engineering

WISE 2018: Web Information Systems Engineering – WISE 2018 pp 408–423Cite as

Bootstrapped Multi-level Distant Supervision for Relation Extraction

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11233))

Abstract

Distant supervised relation extraction has been widely used to identify new relation facts from free text. However, relying on a single-node categorization model to identify relation facts for thousands of relations simultaneously inevitably accompanies with serious false categorization problem. Also to the best of our knowledge, no previous efforts has yet considered to update the categorization model with the new identified relation facts, which wastes the chance to further improsve the extraction precision and recall. In this paper, we novelly propose a multi-level distant supervision model for relation extraction, which divides the original categorization task into a number of sub-tasks in multiple levels of a constructed tree-like categorization structure. With the tree-like structure, an unlabelled relation instance would be categorized step by step along a path from the root node to a leaf node. Beyond that, we propose to do bootstrapped distant supervision to update the distant supervision model with new learned relation facts iteratively to further improve the extraction precision and recall. Experimental results conducted on two real datasets prove that our approach outperforms state-of-the-art approaches by reaching more than 10% better extraction quality.

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Notes

  1. 1.

    http://iesl.cs.umass.edu/riedel/ecml/.

  2. 2.

    http://lemurproject.org/clueweb09/.

  3. 3.

    http://code.google.com/archive/p/word2vec/.

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Acknowledgments

This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016, 61402313, 61472263), and the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003).

Author information

Authors and Affiliations

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

    Ying He & Zhixu Li

  2. Department of Computing, Macquarie University, Sydney, NSW, 2122, Australia

    Guanfeng Liu

  3. CFETS Information Technology (Shanghai) Co., Ltd., Shanghai, China

    Fangfei Cao

  4. IFLYTEK Co., Ltd., Hefei, China

    Zhigang Chen

  5. Migu Co., Ltd., Beijing, China

    Ke Wang & Jie Ma

Authors
  1. Ying He
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  2. Zhixu Li
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  3. Guanfeng Liu
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  4. Fangfei Cao
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  5. Zhigang Chen
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  6. Ke Wang
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  7. Jie Ma
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Corresponding author

Correspondence to Zhixu Li .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. University of Victoria, Footscray, VIC, Australia

    Hua Wang

  4. UNSW Australia, Sydney, NSW, Australia

    Hye-Young Paik

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

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Cite this paper

He, Y. et al. (2018). Bootstrapped Multi-level Distant Supervision for Relation Extraction. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_28

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  • DOI: https://doi.org/10.1007/978-3-030-02922-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02921-0

  • Online ISBN: 978-3-030-02922-7

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