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    <titleInfo>
        <title>On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping</title>
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    <name type="personal">
        <namePart type="given">Mohammad</namePart>
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            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Jill</namePart>
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        <name type="personal">
            <namePart type="given">Christy</namePart>
            <namePart type="family">Doran</namePart>
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        <name type="personal">
            <namePart type="given">Thamar</namePart>
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    <abstract>Meaning conflation deficiency is one of the main limiting factors of word representations which, given their widespread use at the core of many NLP systems, can lead to inaccurate semantic understanding of the input text and inevitably hamper the performance. Sense representations target this problem. However, their potential impact has rarely been investigated in downstream NLP applications. Through a set of experiments on a state-of-the-art reverse dictionary system based on neural networks, we show that a simple adjustment aimed at addressing the meaning conflation deficiency can lead to substantial improvements.</abstract>
    <identifier type="citekey">pilehvar-2019-importance</identifier>
    <identifier type="doi">10.18653/v1/N19-1222</identifier>
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%0 Conference Proceedings
%T On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping
%A Pilehvar, Mohammad Taher
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F pilehvar-2019-importance
%X Meaning conflation deficiency is one of the main limiting factors of word representations which, given their widespread use at the core of many NLP systems, can lead to inaccurate semantic understanding of the input text and inevitably hamper the performance. Sense representations target this problem. However, their potential impact has rarely been investigated in downstream NLP applications. Through a set of experiments on a state-of-the-art reverse dictionary system based on neural networks, we show that a simple adjustment aimed at addressing the meaning conflation deficiency can lead to substantial improvements.
%R 10.18653/v1/N19-1222
%U https://aclanthology.org/N19-1222
%U https://doi.org/10.18653/v1/N19-1222
%P 2151-2156

Markdown (Informal)

[On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping](https://aclanthology.org/N19-1222) (Pilehvar, NAACL 2019)

• On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping (Pilehvar, NAACL 2019)

ACL

• Mohammad Taher Pilehvar. 2019. On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2151–2156, Minneapolis, Minnesota. Association for Computational Linguistics.