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.

What is Done is Done: an Incremental Approach to Semantic Shift Detection / F. Periti, A. Ferrara, S. Montanelli, M. Ruskov - In: LCHANGE / [a cura di] N. Tahmasebi, S. Montariol, A. Kutuzov, S. Hengchen, H. Dubossarsky, L. Borin. - [s.l] : Association for Computational Linguistics (ACL), 2022. - ISBN 9781955917421. - pp. 33-43 (( Intervento presentato al 3. convegno International Workshop on Computational Approaches to Historical Language Change : May, 26th - 27th tenutosi a Dublin nel 2022 [10.18653/v1/2022.lchange-1.4].

What is Done is Done: an Incremental Approach to Semantic Shift Detection

F. Periti
Primo
;
A. Ferrara
Secondo
;
S. Montanelli
Penultimo
;
M. Ruskov
Ultimo
2022

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.
Settore INF/01 - Informatica
2022
Association for Computational Linguistics (ACL)
Book Part (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/961437
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