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    <titleInfo>
        <title>Predicting the Evocation Relation between Lexicalized Concepts</title>
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        <namePart type="given">Yoshihiko</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <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.</abstract>
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        <url>https://aclanthology.org/C16-1156</url>
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        <date>2016-12</date>
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%0 Conference Proceedings
%T Predicting the Evocation Relation between Lexicalized Concepts
%A Hayashi, Yoshihiko
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F hayashi-2016-predicting
%X 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.
%U https://aclanthology.org/C16-1156
%P 1657-1668

Markdown (Informal)

[Predicting the Evocation Relation between Lexicalized Concepts](https://aclanthology.org/C16-1156) (Hayashi, COLING 2016)

• Predicting the Evocation Relation between Lexicalized Concepts (Hayashi, COLING 2016)

ACL

• Yoshihiko Hayashi. 2016. Predicting the Evocation Relation between Lexicalized Concepts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1657–1668, Osaka, Japan. The COLING 2016 Organizing Committee.