Evaluating vector space models using human semantic priming results

Allyson Ettinger, Tal Linzen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Vector space models of word representation are often evaluated using human similarity ratings. Those ratings are elicited in explicit tasks and have well-known subjective biases. As an alternative, we propose evaluating vector spaces using implicit cognitive measures. We focus in particular on semantic priming, exploring the strengths and limitations of existing datasets, and propose ways in which those datasets can be improved.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP, RepEval 2016 at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
    PublisherAssociation for Computational Linguistics (ACL)
    Pages72-77
    Number of pages6
    ISBN (Electronic)9781945626142
    StatePublished - 2016
    Event1st Workshop on Evaluating Vector-Space Representations for NLP, RepEval 2016 at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany
    Duration: Aug 7 2016 → …

    Publication series

    NameProceedings of the Annual Meeting of the Association for Computational Linguistics
    ISSN (Print)0736-587X

    Conference

    Conference1st Workshop on Evaluating Vector-Space Representations for NLP, RepEval 2016 at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
    Country/TerritoryGermany
    CityBerlin
    Period8/7/16 → …

    ASJC Scopus subject areas

    • Computer Science Applications
    • Linguistics and Language
    • Language and Linguistics

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