BibTeX

@inProceedings{cassotti-tahmasebi-2025-hypothesis-355997,
	title        = {A Hypothesis-Driven Framework for Detecting Lexical Semantic Change},
	abstract     = {This paper introduces a hypothesis-driven framework aimed at detecting lexical semantic change, addressing the limitations of current computational methods that struggle with the dynamic and contextually modulated nature of word meanings.
Traditional approaches, such as Word Sense Disambiguation (WSD), fail to capture the fluidity of senses, whereas Word Sense Induction (WSI), while more flexible, lacks the precision necessary to align with predefined semantic structures. Our approach systematically combines expert-defined sense hypotheses with advanced computational techniques, including generative models, encoding and prototyping methods, and targeted semantic analysis. Using words historically significant in scientific contexts—such as theory, gene, and force—we demonstrate the effectiveness of our method in tracing fine semantic changes and metaphorical extensions over time, highlighting its advantages over naive computational strategies.},
	booktitle    = {Proceedings of the Eleventh Italian Conference on Computational Linguistics (CLiC-it 2025)},
	author       = {Cassotti, Pierluigi and Tahmasebi, Nina},
	year         = {2025},
}