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        <title>CogALex-V Shared Task: HsH-Supervised – Supervised similarity learning using entry wise product of context vectors</title>
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            <title>Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)</title>
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        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Zock</namePart>
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        <name type="personal">
            <namePart type="given">Alessandro</namePart>
            <namePart type="family">Lenci</namePart>
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        <name type="personal">
            <namePart type="given">Stefan</namePart>
            <namePart type="family">Evert</namePart>
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    <abstract>The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied.</abstract>
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%0 Conference Proceedings
%T CogALex-V Shared Task: HsH-Supervised – Supervised similarity learning using entry wise product of context vectors
%A Wartena, Christian
%A Aga, Rosa Tsegaye
%Y Zock, Michael
%Y Lenci, Alessandro
%Y Evert, Stefan
%S Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F wartena-aga-2016-cogalex
%X The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied.
%U https://aclanthology.org/W16-5316
%P 114-118

Markdown (Informal)

[CogALex-V Shared Task: HsH-Supervised – Supervised similarity learning using entry wise product of context vectors](https://aclanthology.org/W16-5316) (Wartena & Aga, CogALex 2016)

• CogALex-V Shared Task: HsH-Supervised – Supervised similarity learning using entry wise product of context vectors (Wartena & Aga, CogALex 2016)

ACL

• Christian Wartena and Rosa Tsegaye Aga. 2016. CogALex-V Shared Task: HsH-Supervised – Supervised similarity learning using entry wise product of context vectors. In Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V), pages 114–118, Osaka, Japan. The COLING 2016 Organizing Committee.