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Sparse Bilingual Word Representations for Cross-lingual Lexical Entailment

Yogarshi Vyas,Marine Carpuat

Published 2016 in North American Chapter of the Association for Computational Linguistics

ABSTRACT

We introduce the task of cross-lingual lexical entailment, which aims to detect whether the meaning of a word in one language can be inferred from the meaning of a word in another language. We construct a gold standard for this task, and propose an unsupervised solution based on distributional word representations. As commonly done in the monolingual setting, we assume a worde entails a wordf if the prominent context features of e are a subset of those of f . To address the challenge of comparing contexts across languages, we propose a novel method for inducing sparse bilingual word representations from monolingual and parallel texts. Our approach yields an Fscore of 70%, and significantly outperforms strong baselines based on translation and on existing word representations.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    North American Chapter of the Association for Computational Linguistics

  • Publication date

    2016-06-01

  • Fields of study

    Linguistics, Computer Science

  • Identifiers
    DOI 10.18653/v1/N16-1142
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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