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Learning Word Sense Embeddings from Word Sense Definitions

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Natural Language Understanding and Intelligent Applications (ICCPOL 2016, NLPCC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10102))

Abstract

Word embeddings play a significant role in many modern NLP systems. Since learning one representation per word is problematic for polysemous words and homonymous words, researchers propose to use one embedding per word sense. Their approaches mainly train word sense embeddings on a corpus. In this paper, we propose to use word sense definitions to learn one embedding per word sense. Experimental results on word similarity tasks and a word sense disambiguation task show that word sense embeddings produced by our approach are of high quality.

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Notes

  1. 1.

    http://wordnet.princeton.edu/.

  2. 2.

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

  3. 3.

    https://wordnet.princeton.edu/man/sensemap.5WN.html.

  4. 4.

    http://dumps.wikimedia.org/enwiki/.

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Acknowledgments

This work is supported by National Key Basic Research Program of China under Grant No. 2014CB340504 and National Natural Science Foundation of China under Grant No. 61273318. The Corresponding author of this paper is Baobao Chang.

Author information

Authors and Affiliations

  1. Key Laboratory of Computational Linguistics, Ministry of Education School of Electronics Engineering and Computer Science, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing, 100871, China

    Qi Li, Tianshi Li & Baobao Chang

  2. Collaborative Innovation Center for Language Ability, Xuzhou, 221009, China

    Qi Li, Tianshi Li & Baobao Chang

Authors
  1. Qi Li
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  2. Tianshi Li
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  3. Baobao Chang
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Corresponding author

Correspondence to Baobao Chang .

Editor information

Editors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Chin-Yew Lin

  2. Brandeis University, Waltham, Massachusetts, USA

    Nianwen Xue

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Fudan University, Shanghai, China

    Xuanjing Huang

  5. Peking University, Beijing, China

    Yansong Feng

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Li, Q., Li, T., Chang, B. (2016). Learning Word Sense Embeddings from Word Sense Definitions. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-50496-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50495-7

  • Online ISBN: 978-3-319-50496-4

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