Predicting the evocation relation between lexicalized concepts

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

    1 Citation (Scopus)

    Abstract

    Evocation is a directed yet weighted semantic relationship between lexicalized concepts. Although evocation relations are considered potentially useful in several semantic NLP tasks, the prediction of the evocation relation between an arbitrary pair of concepts remains difficult, since evocation relationships cover a broader range of semantic relations rooted in human perception and experience. This paper presents a supervised learning approach to predict the strength (by regression) and to determine the directionality (by classification) of the evocation relation that might hold between a pair of lexicalized concepts. Empirical results that were obtained by investigating useful features are shown, indicating that a combination of the proposed features largely outperformed individual baselines, and also suggesting that semantic relational vectors computed from existing semantic vectors for lexicalized concepts were indeed effective for both the prediction of strength and the determination of directionality.

    Original languageEnglish
    Title of host publicationCOLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016
    Subtitle of host publicationTechnical Papers
    PublisherAssociation for Computational Linguistics, ACL Anthology
    Pages1657-1668
    Number of pages12
    ISBN (Print)9784879747020
    Publication statusPublished - 2016 Jan 1
    Event26th International Conference on Computational Linguistics, COLING 2016 - Osaka, Japan
    Duration: 2016 Dec 112016 Dec 16

    Other

    Other26th International Conference on Computational Linguistics, COLING 2016
    CountryJapan
    CityOsaka
    Period16/12/1116/12/16

    Fingerprint

    Semantics
    semantics
    Supervised learning
    regression
    learning
    experience
    Directionality
    Prediction

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Language and Linguistics
    • Linguistics and Language

    Cite this

    Hayashi, Y. (2016). Predicting the evocation relation between lexicalized concepts. In COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers (pp. 1657-1668). Association for Computational Linguistics, ACL Anthology.

    Predicting the evocation relation between lexicalized concepts. / Hayashi, Yoshihiko.

    COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers. Association for Computational Linguistics, ACL Anthology, 2016. p. 1657-1668.

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

    Hayashi, Y 2016, Predicting the evocation relation between lexicalized concepts. in COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers. Association for Computational Linguistics, ACL Anthology, pp. 1657-1668, 26th International Conference on Computational Linguistics, COLING 2016, Osaka, Japan, 16/12/11.
    Hayashi Y. Predicting the evocation relation between lexicalized concepts. In COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers. Association for Computational Linguistics, ACL Anthology. 2016. p. 1657-1668
    Hayashi, Yoshihiko. / Predicting the evocation relation between lexicalized concepts. COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers. Association for Computational Linguistics, ACL Anthology, 2016. pp. 1657-1668
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