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National CCF Conference on Natural Language Processing and Chinese Computing

NLPCC 2017: Natural Language Processing and Chinese Computing pp 431–442Cite as

BiLSTM-Based Models for Metaphor Detection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10619))

Abstract

Metaphor is a pervasive phenomenon in our daily use of natural language. Metaphor detection has been playing an important role in a variety of NLP tasks. Most existing approaches to this task rely heavily on the use of human-crafted features built from linguistic knowledge resource, which greatly limits their applicability. This paper presents four BiLSTM-based models for metaphor detection. The first three models use a sub-sequence as the input to BiLSTM network, each with a special kind of sub-sequence extracted from the input sentence. The last model is an ensemble model which aggregate the outputs from the first three models to get the final output. Experimental results have shown the effectiveness of our models.

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Notes

  1. 1.

    http://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm.

  2. 2.

    http://www.cs.sfu.ca/~anoop/students/jbirke/.

  3. 3.

    http://www.cs.cmu.edu/~ark/TurboParser/.

  4. 4.

    https://github.com/ytsvetko/metaphor/blob/master/resources/TroFi/.

  5. 5.

    https://github.com/ytsvetko/metaphor/blob/master/input/.

  6. 6.

    https://github.com/Lasagne/Lasagne.

  7. 7.

    https://github.com/mmihaltz/word2vec-GoogleNews-vectors.

  8. 8.

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

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Acknowledgments

This work is partially supported by National High-Tech R&D Program of China (863 Program) (No. 2015AA015404), the 2016 Civil Aviation Safety Capacity Development Funding Project, and the project “Aircraft Operation Resource Data Exchange and Integration”. We are grateful to the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China

    Shichao Sun & Zhipeng Xie

Authors
  1. Shichao Sun
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  2. Zhipeng Xie
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Corresponding author

Correspondence to Zhipeng Xie .

Editor information

Editors and Affiliations

  1. Fudan University, Shanghai, China

    Xuanjing Huang

  2. Singapore Management University, Singapore, Singapore

    Jing Jiang

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Yansong Feng

  5. Soochow University, Suzhou, China

    Yu Hong

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Sun, S., Xie, Z. (2018). BiLSTM-Based Models for Metaphor Detection. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_36

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  • DOI: https://doi.org/10.1007/978-3-319-73618-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73617-4

  • Online ISBN: 978-3-319-73618-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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