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{ "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/" } ] }