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        <title>What is Done is Done: an Incremental Approach to Semantic Shift Detection</title>
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            <title>Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change</title>
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        <name type="personal">
            <namePart type="given">Nina</namePart>
            <namePart type="family">Tahmasebi</namePart>
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            <namePart type="given">Syrielle</namePart>
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            <namePart type="given">Lars</namePart>
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    <abstract>Contextual word embedding techniques for semantic shift detection are receiving more and more attention. In this paper, we present What is Done is Done (WiDiD), an incremental approach to semantic shift detection based on incremental clustering techniques and contextual embedding methods to capture the changes over the meanings of a target word along a diachronic corpus. In WiDiD, the word contexts observed in the past are consolidated as a set of clusters that constitute the “memory” of the word meanings observed so far. Such a memory is exploited as a basis for subsequent word observations, so that the meanings observed in the present are stratified over the past ones.</abstract>
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    <identifier type="doi">10.18653/v1/2022.lchange-1.4</identifier>
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%0 Conference Proceedings
%T What is Done is Done: an Incremental Approach to Semantic Shift Detection
%A Periti, Francesco
%A Ferrara, Alfio
%A Montanelli, Stefano
%A Ruskov, Martin
%Y Tahmasebi, Nina
%Y Montariol, Syrielle
%Y Kutuzov, Andrey
%Y Hengchen, Simon
%Y Dubossarsky, Haim
%Y Borin, Lars
%S Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F periti-etal-2022-done
%X Contextual word embedding techniques for semantic shift detection are receiving more and more attention. In this paper, we present What is Done is Done (WiDiD), an incremental approach to semantic shift detection based on incremental clustering techniques and contextual embedding methods to capture the changes over the meanings of a target word along a diachronic corpus. In WiDiD, the word contexts observed in the past are consolidated as a set of clusters that constitute the “memory” of the word meanings observed so far. Such a memory is exploited as a basis for subsequent word observations, so that the meanings observed in the present are stratified over the past ones.
%R 10.18653/v1/2022.lchange-1.4
%U https://aclanthology.org/2022.lchange-1.4
%U https://doi.org/10.18653/v1/2022.lchange-1.4
%P 33-43

Markdown (Informal)

[What is Done is Done: an Incremental Approach to Semantic Shift Detection](https://aclanthology.org/2022.lchange-1.4) (Periti et al., LChange 2022)

• What is Done is Done: an Incremental Approach to Semantic Shift Detection (Periti et al., LChange 2022)

ACL

• Francesco Periti, Alfio Ferrara, Stefano Montanelli, and Martin Ruskov. 2022. What is Done is Done: an Incremental Approach to Semantic Shift Detection. In Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change, pages 33–43, Dublin, Ireland. Association for Computational Linguistics.