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Entity Synonym Discovery via Multiple Attentions

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Semantic Technology (JIST 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12032))

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Abstract

Entity synonym discovery is an important task, and it can benefit many downstream applications, such as web search, question answering and knowledge graph construction. Two types of approaches are widely exploited to discover synonyms from a raw text corpus, including the distributional based approaches and pattern based approaches. However, they suffered from either low precision or low recall. In this paper, we propose a novel framework SynMine to extract synonyms from massive raw text corpora. The framework can integrate corpus-level statistics and local contexts in a unified way via a multi-attention mechanism. Extensive experiments on a real-world dataset show the effectiveness of our approach.

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Notes

  1. 1.

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

  2. 2.

    https://baike.baidu.com/.

  3. 3.

    http://hanlp.com/.

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Acknowledgements

This work is supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY17F020015), the Fundamental Research Funds for the Central Universities (No. 2019FZA5013), the Chinese Knowledge Center of Engineering Science and Technology (CKCEST) and MOE Engineering Research Center of Digital Library.

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Authors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, 310027, China

    Jiale Yu, Weiming Lu, Wei Xu & Zeyun Tang

Authors
  1. Jiale Yu
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  2. Weiming Lu
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  3. Wei Xu
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  4. Zeyun Tang
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Corresponding author

Correspondence to Weiming Lu .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Bari, Bari, Italy

    Francesca Alessandra Lisi

  3. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Guohui Xiao

  4. Imperial College London, London, UK

    Elena Botoeva

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Yu, J., Lu, W., Xu, W., Tang, Z. (2020). Entity Synonym Discovery via Multiple Attentions. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Lecture Notes in Computer Science(), vol 12032. Springer, Cham. https://doi.org/10.1007/978-3-030-41407-8_18

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  • DOI: https://doi.org/10.1007/978-3-030-41407-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41406-1

  • Online ISBN: 978-3-030-41407-8

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