{"pk":27837,"title":"Analyzing and modeling free word associations","subtitle":null,"abstract":"Human free association (FA) norms are believed to reflect thestrength of links between words in the lexicon of an averagespeaker. Large-scale FA norms are commonly used as a datasource both in psycholinguistics and in computational mod-eling. However, few studies aim to analyze FA norms them-selves, and it is not known what are the most important factorsthat guide speakers’ lexical choices in the FA task. Here, wefirst provide a statistical analysis of a large-scale data set ofEnglish FA norms. Second, we argue that such analysis caninform existing computational models of semantic memory,and present a case study with the topic model to support thisclaim. Based on our analysis, we provide the topic model withdictionary-based knowledge about word synonymy/antonymy,and demonstrate that the resulting model predicts human FAresponses better than the topic model without this information.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Free association"},{"word":"Semantic memory"},{"word":"Statistical Modeling"},{"word":"topic model"},{"word":"Latent Dirichlet allocation"}],"section":"Publication-based-Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/7h52305w","frozenauthors":[{"first_name":"Yevgen","middle_name":"","last_name":"Matusevych","name_suffix":"","institution":"U of Toronto","department":""},{"first_name":"Suzanne","middle_name":"","last_name":"Stevenson","name_suffix":"","institution":"U of Toronto","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2018-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27837/galley/17476/download/"}]}