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        <title>Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change</title>
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        <namePart type="given">Haim</namePart>
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    <name type="personal">
        <namePart type="given">Dominik</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1044</identifier>
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%0 Conference Proceedings
%T Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
%A Dubossarsky, Haim
%A Hengchen, Simon
%A Tahmasebi, Nina
%A Schlechtweg, Dominik
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F dubossarsky-etal-2019-time
%X State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.
%R 10.18653/v1/P19-1044
%U https://aclanthology.org/P19-1044
%U https://doi.org/10.18653/v1/P19-1044
%P 457-470

Markdown (Informal)

[Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change](https://aclanthology.org/P19-1044) (Dubossarsky et al., ACL 2019)

• Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (Dubossarsky et al., ACL 2019)

ACL

• Haim Dubossarsky, Simon Hengchen, Nina Tahmasebi, and Dominik Schlechtweg. 2019. Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 457–470, Florence, Italy. Association for Computational Linguistics.