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Exploiting Synonymy and Hypernymy to Learn Efficient Meaning Representations

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 10075)


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.


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

This work was supported by JSPS KAKENHI Grants Number 15H02754 and 16K12546.

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Correspondence to Thomas Perianin .

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Perianin, T., Senuma, H., Aizawa, A. (2016). Exploiting Synonymy and Hypernymy to Learn Efficient Meaning Representations. In: Morishima, A., Rauber, A., Liew, C. (eds) Digital Libraries: Knowledge, Information, and Data in an Open Access Society. ICADL 2016. Lecture Notes in Computer Science(), vol 10075. Springer, Cham.

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