Predicting the evocation relation between lexicalized concepts

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルCOLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016
ホスト出版物のサブタイトルTechnical Papers
出版社Association for Computational Linguistics, ACL Anthology
ページ1657-1668
ページ数12
ISBN(印刷版)9784879747020
出版ステータスPublished - 2016
イベント26th International Conference on Computational Linguistics, COLING 2016 - Osaka, Japan
継続期間: 2016 12 112016 12 16

出版物シリーズ

名前COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers

Other

Other26th International Conference on Computational Linguistics, COLING 2016
国/地域Japan
CityOsaka
Period16/12/1116/12/16

ASJC Scopus subject areas

  • 計算理論と計算数学
  • 言語および言語学
  • 言語学および言語

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