Analyzing and modeling free word associations

AbstractHuman 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.

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