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        <title>Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations</title>
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    <name type="personal">
        <namePart type="given">Yoshihiko</namePart>
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            <namePart type="given">Jose</namePart>
            <namePart type="family">Camacho-Collados</namePart>
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    <abstract>This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming: (1) a specific type of lexical-semantic relation between a pair of words would be indicated by a carefully designed set of relation-specific similarities associated with the words; and (2) the similarities could be effectively computed by “sense representations” (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations.</abstract>
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    <identifier type="doi">10.18653/v1/W17-1905</identifier>
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%0 Conference Proceedings
%T Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations
%A Kanada, Kentaro
%A Kobayashi, Tetsunori
%A Hayashi, Yoshihiko
%Y Camacho-Collados, Jose
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F kanada-etal-2017-classifying
%X This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming: (1) a specific type of lexical-semantic relation between a pair of words would be indicated by a carefully designed set of relation-specific similarities associated with the words; and (2) the similarities could be effectively computed by “sense representations” (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations.
%R 10.18653/v1/W17-1905
%U https://aclanthology.org/W17-1905
%U https://doi.org/10.18653/v1/W17-1905
%P 37-46

Markdown (Informal)

[Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations](https://aclanthology.org/W17-1905) (Kanada et al., SENSE 2017)

• Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations (Kanada et al., SENSE 2017)

ACL

• Kentaro Kanada, Tetsunori Kobayashi, and Yoshihiko Hayashi. 2017. Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations. In Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pages 37–46, Valencia, Spain. Association for Computational Linguistics.