{"pk":49356,"title":"Fine-tuning semantic vectors with semantic fluency data","subtitle":null,"abstract":"Semantic vectors derived from training on large text corpora (e.g., word2vec, BERT) are widely used as a methodological tool to model similarity of concepts. Recent work has demonstrated that a small amount of human training data can be used to fine-tune these vectors for modeling specific tasks. For example, human ratings of pairwise similarity can be used to estimate a set of dimensional weights, and these weights can improve estimates of human similarity ratings for held-out pairs. We applied this methodology to the semantic fluency task (listing items from a category) and find that category- specific weights can be used to identify the semantic category of a fluency list. The results have methodological implications for modeling retrieval in semantic fluency tasks, estimating semantic representations, and identifying semantic clusters and switches in fluency data.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Linguistics; Psychology; Memory; Representation; Semantics of language; Computational Modeling; Knowledge representation"}],"section":"Papers with Poster Presentation","is_remote":true,"remote_url":"https://escholarship.org/uc/item/06p223vj","frozenauthors":[{"first_name":"Jeffrey","middle_name":"","last_name":"Zemla","name_suffix":"","institution":"Syracuse University","department":""},{"first_name":"Nichol","middle_name":"","last_name":"Castro","name_suffix":"","institution":"University at Buffalo","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2025-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/49356/galley/37317/download/"}]}