@inproceedings{goworek-dubossarsky-2024-toward,
title = "Toward Sentiment Aware Semantic Change Analysis",
author = "Goworek, Roksana and
Dubossarsky, Haim",
editor = "Falk, Neele and
Papi, Sara and
Zhang, Mike",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-srw.28",
pages = "350--357",
abstract = "This student paper explores the potential of augmenting computational models of semantic change with sentiment information. It tests the efficacy of this approach on the English SemEval of Lexical Semantic Change and its associated historical corpora. We first establish the feasibility of our approach by demonstrating that existing models extract reliable sentiment information from historical corpora, and then validate that words that underwent semantic change also show greater sentiment change in comparison to historically stable words. We then integrate sentiment information into standard models of semantic change for individual words, and test if this can improve the overall performance of the latter, showing mixed results. This research contributes to our understanding of language change by providing the first attempt to enrich standard models of semantic change with additional information. It taps into the multifaceted nature of language change, that should not be reduced only to binary or scalar report of change, but adds additional dimensions to this change, sentiment being only one of these. As such, this student paper suggests novel directions for future work in integrating additional, more nuanced information of change and interpretation for finer-grained semantic change analysis.",
}
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%0 Conference Proceedings
%T Toward Sentiment Aware Semantic Change Analysis
%A Goworek, Roksana
%A Dubossarsky, Haim
%Y Falk, Neele
%Y Papi, Sara
%Y Zhang, Mike
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F goworek-dubossarsky-2024-toward
%X This student paper explores the potential of augmenting computational models of semantic change with sentiment information. It tests the efficacy of this approach on the English SemEval of Lexical Semantic Change and its associated historical corpora. We first establish the feasibility of our approach by demonstrating that existing models extract reliable sentiment information from historical corpora, and then validate that words that underwent semantic change also show greater sentiment change in comparison to historically stable words. We then integrate sentiment information into standard models of semantic change for individual words, and test if this can improve the overall performance of the latter, showing mixed results. This research contributes to our understanding of language change by providing the first attempt to enrich standard models of semantic change with additional information. It taps into the multifaceted nature of language change, that should not be reduced only to binary or scalar report of change, but adds additional dimensions to this change, sentiment being only one of these. As such, this student paper suggests novel directions for future work in integrating additional, more nuanced information of change and interpretation for finer-grained semantic change analysis.
%U https://aclanthology.org/2024.eacl-srw.28
%P 350-357
Markdown (Informal)
[Toward Sentiment Aware Semantic Change Analysis](https://aclanthology.org/2024.eacl-srw.28) (Goworek & Dubossarsky, EACL 2024)
ACL
- Roksana Goworek and Haim Dubossarsky. 2024. Toward Sentiment Aware Semantic Change Analysis. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 350–357, St. Julian’s, Malta. Association for Computational Linguistics.