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        <title>Enhancing the Context Representation in Similarity-based Word Sense Disambiguation</title>
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    <name type="personal">
        <namePart type="given">Ming</namePart>
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    <name type="personal">
        <namePart type="given">Jianzhang</namePart>
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    <name type="personal">
        <namePart type="given">Yinglin</namePart>
        <namePart type="family">Wang</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>In previous similarity-based WSD systems, studies have allocated much effort on learning comprehensive sense embeddings using contextual representations and knowledge sources. However, the context embedding of an ambiguous word is learned using only the sentence where the word appears, neglecting its global context. In this paper, we investigate the contribution of both word-level and sense-level global context of an ambiguous word for disambiguation. Experiments have shown that the Context-Oriented Embedding (COE) can enhance a similarity-based system’s performance on WSD by relatively large margins, achieving state-of-the-art on all-words WSD benchmarks in knowledge-based category.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.706</identifier>
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%0 Conference Proceedings
%T Enhancing the Context Representation in Similarity-based Word Sense Disambiguation
%A Wang, Ming
%A Zhang, Jianzhang
%A Wang, Yinglin
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F wang-etal-2021-enhancing-context
%X In previous similarity-based WSD systems, studies have allocated much effort on learning comprehensive sense embeddings using contextual representations and knowledge sources. However, the context embedding of an ambiguous word is learned using only the sentence where the word appears, neglecting its global context. In this paper, we investigate the contribution of both word-level and sense-level global context of an ambiguous word for disambiguation. Experiments have shown that the Context-Oriented Embedding (COE) can enhance a similarity-based system’s performance on WSD by relatively large margins, achieving state-of-the-art on all-words WSD benchmarks in knowledge-based category.
%R 10.18653/v1/2021.emnlp-main.706
%U https://aclanthology.org/2021.emnlp-main.706
%U https://doi.org/10.18653/v1/2021.emnlp-main.706
%P 8965-8973

Markdown (Informal)

[Enhancing the Context Representation in Similarity-based Word Sense Disambiguation](https://aclanthology.org/2021.emnlp-main.706) (Wang et al., EMNLP 2021)

• Enhancing the Context Representation in Similarity-based Word Sense Disambiguation (Wang et al., EMNLP 2021)

ACL

• Ming Wang, Jianzhang Zhang, and Yinglin Wang. 2021. Enhancing the Context Representation in Similarity-based Word Sense Disambiguation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8965–8973, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.