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        <title>How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis</title>
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    <name type="personal">
        <namePart type="given">Ivan</namePart>
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            <title>Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers</title>
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        <name type="personal">
            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
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            <namePart type="given">Phil</namePart>
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        <name type="personal">
            <namePart type="given">Alexander</namePart>
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    <abstract>One key property of word embeddings currently under study is their capacity to encode hypernymy. Previous works have used supervised models to recover hypernymy structures from embeddings. However, the overall results do not clearly show how well we can recover such structures. We conduct the first dataset-centric analysis that shows how only the Baroni dataset provides consistent results. We empirically show that a possible reason for its good performance is its alignment to dimensions specific of hypernymy: generality and similarity</abstract>
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%0 Conference Proceedings
%T How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis
%A Sanchez, Ivan
%A Riedel, Sebastian
%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 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F sanchez-riedel-2017-well
%X One key property of word embeddings currently under study is their capacity to encode hypernymy. Previous works have used supervised models to recover hypernymy structures from embeddings. However, the overall results do not clearly show how well we can recover such structures. We conduct the first dataset-centric analysis that shows how only the Baroni dataset provides consistent results. We empirically show that a possible reason for its good performance is its alignment to dimensions specific of hypernymy: generality and similarity
%U https://aclanthology.org/E17-2064
%P 401-407

Markdown (Informal)

[How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis](https://aclanthology.org/E17-2064) (Sanchez & Riedel, EACL 2017)

• How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis (Sanchez & Riedel, EACL 2017)

ACL

• Ivan Sanchez and Sebastian Riedel. 2017. How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 401–407, Valencia, Spain. Association for Computational Linguistics.