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        <title>Word Sense Filtering Improves Embedding-Based Lexical Substitution</title>
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    <name type="personal">
        <namePart type="given">Anne</namePart>
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    <name type="personal">
        <namePart type="given">Marianna</namePart>
        <namePart type="family">Apidianaki</namePart>
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    <name type="personal">
        <namePart type="given">Chris</namePart>
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            <title>Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications</title>
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        <name type="personal">
            <namePart type="given">Jose</namePart>
            <namePart type="family">Camacho-Collados</namePart>
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            <namePart type="given">Mohammad</namePart>
            <namePart type="given">Taher</namePart>
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    <abstract>The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense. We show that a filtering mechanism based on a sense inventory optimized for substitutability can improve the results of these models. Our sense inventory is constructed using a clustering method which generates paraphrase clusters that are congruent with lexical substitution annotations in a development set. The results show that lexical substitution can still benefit from senses which can improve the output of vector space paraphrase ranking models.</abstract>
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    <identifier type="doi">10.18653/v1/W17-1914</identifier>
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%0 Conference Proceedings
%T Word Sense Filtering Improves Embedding-Based Lexical Substitution
%A Cocos, Anne
%A Apidianaki, Marianna
%A Callison-Burch, Chris
%Y Camacho-Collados, Jose
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F cocos-etal-2017-word
%X The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense. We show that a filtering mechanism based on a sense inventory optimized for substitutability can improve the results of these models. Our sense inventory is constructed using a clustering method which generates paraphrase clusters that are congruent with lexical substitution annotations in a development set. The results show that lexical substitution can still benefit from senses which can improve the output of vector space paraphrase ranking models.
%R 10.18653/v1/W17-1914
%U https://aclanthology.org/W17-1914
%U https://doi.org/10.18653/v1/W17-1914
%P 110-119

Markdown (Informal)

[Word Sense Filtering Improves Embedding-Based Lexical Substitution](https://aclanthology.org/W17-1914) (Cocos et al., SENSE 2017)

• Word Sense Filtering Improves Embedding-Based Lexical Substitution (Cocos et al., SENSE 2017)

ACL

• Anne Cocos, Marianna Apidianaki, and Chris Callison-Burch. 2017. Word Sense Filtering Improves Embedding-Based Lexical Substitution. In Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pages 110–119, Valencia, Spain. Association for Computational Linguistics.