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        <title>Embedding Words and Senses Together via Joint Knowledge-Enhanced Training</title>
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            <namePart type="family">Levy</namePart>
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            <namePart type="given">Lucia</namePart>
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    <abstract>Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora. However, their creation process does not allow the different meanings of a word to be automatically separated, as it conflates them into a single vector. We address this issue by proposing a new model which learns word and sense embeddings jointly. Our model exploits large corpora and knowledge from semantic networks in order to produce a unified vector space of word and sense embeddings. We evaluate the main features of our approach both qualitatively and quantitatively in a variety of tasks, highlighting the advantages of the proposed method in comparison to state-of-the-art word- and sense-based models.</abstract>
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%0 Conference Proceedings
%T Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
%A Mancini, Massimiliano
%A Camacho-Collados, Jose
%A Iacobacci, Ignacio
%A Navigli, Roberto
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F mancini-etal-2017-embedding
%X Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora. However, their creation process does not allow the different meanings of a word to be automatically separated, as it conflates them into a single vector. We address this issue by proposing a new model which learns word and sense embeddings jointly. Our model exploits large corpora and knowledge from semantic networks in order to produce a unified vector space of word and sense embeddings. We evaluate the main features of our approach both qualitatively and quantitatively in a variety of tasks, highlighting the advantages of the proposed method in comparison to state-of-the-art word- and sense-based models.
%R 10.18653/v1/K17-1012
%U https://aclanthology.org/K17-1012
%U https://doi.org/10.18653/v1/K17-1012
%P 100-111

Markdown (Informal)

[Embedding Words and Senses Together via Joint Knowledge-Enhanced Training](https://aclanthology.org/K17-1012) (Mancini et al., CoNLL 2017)

• Embedding Words and Senses Together via Joint Knowledge-Enhanced Training (Mancini et al., CoNLL 2017)

ACL

• Massimiliano Mancini, Jose Camacho-Collados, Ignacio Iacobacci, and Roberto Navigli. 2017. Embedding Words and Senses Together via Joint Knowledge-Enhanced Training. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 100–111, Vancouver, Canada. Association for Computational Linguistics.