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Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change

Conference paper
Authors Haim Dubossarsky
Simon Hengchen
Nina Tahmasebi
Dominik Schlechtweg
Published in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, July 28 - August 2, 2019, pp. 457–470
ISSN 0891-2017
Publisher Association for Computational Linguistics
Publication year 2019
Published at Department of Swedish
Language en
Links https://www.aclweb.org/anthology/P1...
Keywords Lexical semantic change, evaluation, data science
Subject categories Information technology, Language Technology (Computational Linguistics)

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.

Page Manager: Webmaster|Last update: 9/11/2012
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