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        <title>Training Word Sense Embeddings With Lexicon-based Regularization</title>
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        <namePart type="given">Luis</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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    <abstract>We propose to improve word sense embeddings by enriching an automatic corpus-based method with lexicographic data. Information from a lexicon is introduced into the learning algorithm’s objective function through a regularizer. The incorporation of lexicographic data yields embeddings that are able to reflect expert-defined word senses, while retaining the robustness, high quality, and coverage of automatic corpus-based methods. These properties are observed in a manual inspection of the semantic clusters that different degrees of regularizer strength create in the vector space. Moreover, we evaluate the sense embeddings in two downstream applications: word sense disambiguation and semantic frame prediction, where they outperform simpler approaches. Our results show that a corpus-based model balanced with lexicographic data learns better representations and improve their performance in downstream tasks.</abstract>
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%0 Conference Proceedings
%T Training Word Sense Embeddings With Lexicon-based Regularization
%A Nieto-Piña, Luis
%A Johansson, Richard
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F nieto-pina-johansson-2017-training
%X We propose to improve word sense embeddings by enriching an automatic corpus-based method with lexicographic data. Information from a lexicon is introduced into the learning algorithm’s objective function through a regularizer. The incorporation of lexicographic data yields embeddings that are able to reflect expert-defined word senses, while retaining the robustness, high quality, and coverage of automatic corpus-based methods. These properties are observed in a manual inspection of the semantic clusters that different degrees of regularizer strength create in the vector space. Moreover, we evaluate the sense embeddings in two downstream applications: word sense disambiguation and semantic frame prediction, where they outperform simpler approaches. Our results show that a corpus-based model balanced with lexicographic data learns better representations and improve their performance in downstream tasks.
%U https://aclanthology.org/I17-1029
%P 284-294

Markdown (Informal)

[Training Word Sense Embeddings With Lexicon-based Regularization](https://aclanthology.org/I17-1029) (Nieto-Piña & Johansson, IJCNLP 2017)

• Training Word Sense Embeddings With Lexicon-based Regularization (Nieto-Piña & Johansson, IJCNLP 2017)

ACL

• Luis Nieto-Piña and Richard Johansson. 2017. Training Word Sense Embeddings With Lexicon-based Regularization. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 284–294, Taipei, Taiwan. Asian Federation of Natural Language Processing.