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    <titleInfo>
        <title>Fitting Semantic Relations to Word Embeddings</title>
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        <namePart type="given">Eric</namePart>
        <namePart type="family">Kafe</namePart>
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            <title>Proceedings of the 10th Global Wordnet Conference</title>
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        <name type="personal">
            <namePart type="given">Piek</namePart>
            <namePart type="family">Vossen</namePart>
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        <name type="personal">
            <namePart type="given">Christiane</namePart>
            <namePart type="family">Fellbaum</namePart>
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    <abstract>We fit WordNet relations to word embeddings, using 3CosAvg and LRCos, two set-based methods for analogy resolution, and introduce 3CosWeight, a new, weighted variant of 3CosAvg. We test the performance of the resulting semantic vectors in lexicographic semantics tests, and show that none of the tested classifiers can learn symmetric relations like synonymy and antonymy, since the source and target words of these relations are the same set. By contrast, with the asymmetric relations (hyperonymy / hyponymy and meronymy), both 3CosAvg and LRCos clearly outperform the baseline in all cases, while 3CosWeight attained the best scores with hyponymy and meronymy, suggesting that this new method could provide a useful alternative to previous approaches.</abstract>
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%0 Conference Proceedings
%T Fitting Semantic Relations to Word Embeddings
%A Kafe, Eric
%Y Vossen, Piek
%Y Fellbaum, Christiane
%S Proceedings of the 10th Global Wordnet Conference
%D 2019
%8 July
%I Global Wordnet Association
%C Wroclaw, Poland
%F kafe-2019-fitting
%X We fit WordNet relations to word embeddings, using 3CosAvg and LRCos, two set-based methods for analogy resolution, and introduce 3CosWeight, a new, weighted variant of 3CosAvg. We test the performance of the resulting semantic vectors in lexicographic semantics tests, and show that none of the tested classifiers can learn symmetric relations like synonymy and antonymy, since the source and target words of these relations are the same set. By contrast, with the asymmetric relations (hyperonymy / hyponymy and meronymy), both 3CosAvg and LRCos clearly outperform the baseline in all cases, while 3CosWeight attained the best scores with hyponymy and meronymy, suggesting that this new method could provide a useful alternative to previous approaches.
%U https://aclanthology.org/2019.gwc-1.29
%P 228-237

Markdown (Informal)

[Fitting Semantic Relations to Word Embeddings](https://aclanthology.org/2019.gwc-1.29) (Kafe, GWC 2019)

• Fitting Semantic Relations to Word Embeddings (Kafe, GWC 2019)

ACL

• Eric Kafe. 2019. Fitting Semantic Relations to Word Embeddings. In Proceedings of the 10th Global Wordnet Conference, pages 228–237, Wroclaw, Poland. Global Wordnet Association.