Analyzing and modeling free word associations

Yevgen Matusevych, Suzanne Stevenson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Human free association (FA) norms are believed to reflect the strength of links between words in the lexicon of an average speaker. Large-scale FA norms are commonly used as a data source both in psycholinguistics and in computational modeling. However, few studies aim to analyze FA norms themselves, and it is not known what are the most important factors that guide speakers’ lexical choices in the FA task. Here, we first provide a statistical analysis of a large-scale data set of English FA norms. Second, we argue that such analysis can inform existing computational models of semantic memory, and present a case study with the topic model to support this claim. Based on our analysis, we provide the topic model with dictionary-based knowledge about word synonymy/antonymy, and demonstrate that the resulting model predicts human FA responses better than the topic model without this information.
Original languageEnglish
Title of host publicationProceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018)
EditorsChuck Kalish, Marina Rau, Tim Rogers, Jerry Zhu
PublisherCognitive Science Society
Number of pages6
ISBN (Electronic)978-0-9911967-8-4
ISBN (Print)978-1-5108-7205-9
Publication statusPublished - 1 Jan 2019
Event40th Annual Meeting of the Cognitive Science Society - Madison, United States
Duration: 25 Jul 201828 Jul 2018


Conference40th Annual Meeting of the Cognitive Science Society
Abbreviated titleCogSci 2018
Country/TerritoryUnited States
Internet address


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