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Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched Wikipedia

Claudio Delli Bovi,Alessandro Raganato

Published 2017 in International Workshop on Semantic Evaluation

ABSTRACT

This paper describes Sew-Embed, our language-independent approach to multilingual and cross-lingual semantic word similarity as part of the SemEval-2017 Task 2. We leverage the Wikipedia-based concept representations developed by Raganato et al. (2016), and propose an embedded augmentation of their explicit high-dimensional vectors, which we obtain by plugging in an arbitrary word (or sense) embedding representation, and computing a weighted average in the continuous vector space. We evaluate Sew-Embed with two different off-the-shelf embedding representations, and report their performances across all monolingual and cross-lingual benchmarks available for the task. Despite its simplicity, especially compared with supervised or overly tuned approaches, Sew-Embed achieves competitive results in the cross-lingual setting (3rd best result in the global ranking of subtask 2, score 0.56).

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    International Workshop on Semantic Evaluation

  • Publication date

    2017-08-01

  • Fields of study

    Linguistics, Computer Science

  • Identifiers
    DOI 10.18653/v1/S17-2041
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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CLAIMS

  • No claims are published for this paper.

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