@inproceedings{kurtyigit-etal-2021-lexical,
title = "Lexical Semantic Change Discovery",
author = "Kurtyigit, Sinan and
Park, Maike and
Schlechtweg, Dominik and
Kuhn, Jonas and
Schulte im Walde, Sabine",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.543",
doi = "10.18653/v1/2021.acl-long.543",
pages = "6985--6998",
abstract = "While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.",
}
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<abstract>While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.</abstract>
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%0 Conference Proceedings
%T Lexical Semantic Change Discovery
%A Kurtyigit, Sinan
%A Park, Maike
%A Schlechtweg, Dominik
%A Kuhn, Jonas
%A Schulte im Walde, Sabine
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F kurtyigit-etal-2021-lexical
%X While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.
%R 10.18653/v1/2021.acl-long.543
%U https://aclanthology.org/2021.acl-long.543
%U https://doi.org/10.18653/v1/2021.acl-long.543
%P 6985-6998
Markdown (Informal)
[Lexical Semantic Change Discovery](https://aclanthology.org/2021.acl-long.543) (Kurtyigit et al., ACL-IJCNLP 2021)
ACL
- Sinan Kurtyigit, Maike Park, Dominik Schlechtweg, Jonas Kuhn, and Sabine Schulte im Walde. 2021. Lexical Semantic Change Discovery. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6985–6998, Online. Association for Computational Linguistics.