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Exploiting Synonymy and Hypernymy to Learn Efficient Meaning Representations

  • Thomas Perianin
  • Hajime Senuma
  • Akiko Aizawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10075)

Abstract

Word representation learning methods such as word2vec usually associate one vector per word; however, in order to face polysemy problems, it’s important to produce distributed representations for each meaning, not for each surface form of a word. In this paper, we propose an extension for the existing AutoExtend model, an auto-encoder architecture that utilises synonymy relations to learn sense representations. We introduce a new layer in the architecture to exploit hypernymy relations predominantly present in existing ontologies. We evaluate the quality of the obtained vectors on word-sense disambiguation tasks and show that the use of the hypernymy relation leads to improvements of 1.2 % accuracy on Senseval-3 and 0.8 % on Semeval-2007 English lexical sample tasks, compared to the original model.

Keywords

Sense embedding Semantic relation Auto-encoder Hypernymy Word-sense disambiguation 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Thomas Perianin
    • 1
    • 3
  • Hajime Senuma
    • 2
  • Akiko Aizawa
    • 2
    • 3
  1. 1.Université Pierre et Marie CurieParisFrance
  2. 2.The University of TokyoBunkyoJapan
  3. 3.National Institute of InformaticsChiyodaJapan

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