ArticleJournal of cognition2018
Predicting Lexical Norms: A Comparison between a Word Association Model and Text-Based Word Co-occurrence Models.
Article in Journal of cognition, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Contextual assembly of lexical functions in large language models.Behavior research methods · 2025Article
- The "LLM World of Words" English free association norms generated by large language models.Scientific data · 2025Article
- Mapping Mental Representations With Free Associations: A Tutorial Using the R Package associatoR.Journal of cognition · 2025Article
- Extrapolation of affective norms using transformer-based neural networks and its application to experimental stimuli selection.Behavior research methods · 2024Article
- Concreteness ratings for 36,000 Estonian words.Behavior research methods · 2024Article
- Novel embeddings improve the prediction of risk perception.EPJ data science · 2024Article
- Semantic gender: Norms for 24,000 Dutch words and its role in word meaning.Behavior research methods · 2024Article
- Independency of Coding for Affective Similarities and for Word Co-occurrences in Temporal Perisylvian Neocortex.Neurobiology of language (Cambridge, Mass.) · 2023Article
- On the semantic representation of risk.Science advances · 2022Article
- Pandemic Critical Care Research during the COVID-19 (2020-2022): A Bibliometric Analysis Using VOSviewer.BioMed research international · 2022Review
- Article
- Knowledge Representations Derived From Semantic Fluency Data.Frontiers in psychology · 2022Review
- Beauty and Wellness in the Semantic Memory of the Beholder.Frontiers in psychology · 2021Article
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4 authors.
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Abstract
In two studies we compare a distributional semantic model derived from word co-occurrences and a word association based model in their ability to predict properties that affect lexical processing. We focus on age of acquisition, concreteness, and three affective variables, namely valence, arousal, and dominance, since all these variables have been shown to be fundamental in word meaning. In both studies we use a model based on data obtained in a continued free word association task to predict these variables. In Study 1 we directly compare this model to a word co-occurrence model based on syntactic dependency relations to see which model is better at predicting the variables under scrutiny in Dutch. In Study 2 we replicate our findings in English and compare our results to those reported in the literature. In both studies we find the word association-based model fit to predict diverse word properties. Especially in the case of predicting affective word properties, we show that the association model is superior to the distributional model.
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