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        <title>Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection</title>
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    <abstract>The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution. We investigate an extensive number of such unsupervised measures, using several distributional semantic models that differ by context type and feature weighting. We analyze the performance of the different methods based on their linguistic motivation. Comparison to the state-of-the-art supervised methods shows that while supervised methods generally outperform the unsupervised ones, the former are sensitive to the distribution of training instances, hurting their reliability. Being based on general linguistic hypotheses and independent from training data, unsupervised measures are more robust, and therefore are still useful artillery for hypernymy detection.</abstract>
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%0 Conference Proceedings
%T Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
%A Shwartz, Vered
%A Santus, Enrico
%A Schlechtweg, Dominik
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F shwartz-etal-2017-hypernyms
%X The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution. We investigate an extensive number of such unsupervised measures, using several distributional semantic models that differ by context type and feature weighting. We analyze the performance of the different methods based on their linguistic motivation. Comparison to the state-of-the-art supervised methods shows that while supervised methods generally outperform the unsupervised ones, the former are sensitive to the distribution of training instances, hurting their reliability. Being based on general linguistic hypotheses and independent from training data, unsupervised measures are more robust, and therefore are still useful artillery for hypernymy detection.
%U https://aclanthology.org/E17-1007
%P 65-75

Markdown (Informal)

[Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection](https://aclanthology.org/E17-1007) (Shwartz et al., EACL 2017)

• Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection (Shwartz et al., EACL 2017)

ACL

• Vered Shwartz, Enrico Santus, and Dominik Schlechtweg. 2017. Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 65–75, Valencia, Spain. Association for Computational Linguistics.