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Background Semantic Information Improves Verbal Metaphor Identification

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Natural Language Processing and Chinese Computing (NLPCC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13029))

Abstract

Metaphor is frequently applied in human language. In natural language processing field, metaphor identification has long been studied. In this work, we focus on the verbal metaphor identification. Based on the observation that verbal metaphor occurs on the iteration between the verb and its subject/object, we propose to leverage the abundant information of the sentences containing the verb, named as background semantic information. We devise to leverage the background knowledge to improve verbal metaphor identification, and obtain a state-of-the-art performance in two public verbal metaphor identification datasets, MOH_X and Trofi. Further experiment analyses verify the effectiveness of our proposed method.

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Notes

  1. 1.

    http://nlp.stanford.edu/data/WestburyLab.wikicorp.201004.txt.bz2.

  2. 2.

    Natural Language Toolkit (NLTK, http://www.nltk.org/).

  3. 3.

    https://github.com/biplab-iitb/practNLPTools.

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Acknowledgments

We thank the reviewers for their valuable comments. This work was supported by the National Key Research and Development Program of China (No. 2020AAA0106602).

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Authors and Affiliations

  1. Center for Data Science, AAIS, Peking University, Beijing, China

    Wentao Qin & Dongyan Zhao

  2. Wangxuan Institute of Computer Technology, Peking University, Beijing, China

    Wentao Qin & Dongyan Zhao

Authors
  1. Wentao Qin
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  2. Dongyan Zhao
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Corresponding author

Correspondence to Dongyan Zhao .

Editor information

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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Qin, W., Zhao, D. (2021). Background Semantic Information Improves Verbal Metaphor Identification. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13029. Springer, Cham. https://doi.org/10.1007/978-3-030-88483-3_22

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  • DOI: https://doi.org/10.1007/978-3-030-88483-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88482-6

  • Online ISBN: 978-3-030-88483-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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