LSC-Eval: A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Data
Poster presented at ACL 2025. An evaluation framework that generates historical synthetic benchmark datasets for testing whether semantic change methods are sensitive to detecting the kinds of change they claim to measure.
Jul 28, 2025
SIBling: A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications
Poster presented at IC2S2 2025. The paper proposes SIBling, a three-dimensional computational linguistic framework for evaluating lexical semantic change across Sentiment, Intensity, and Breadth, illustrated through an analysis of how 'mental health' and 'mental illness' have shifted in meaning across two corpora.
Jul 2, 2025