Predicting human similarity judgments with distributional models: The value of word associations

Abstract

To represent the meaning of a word, most models use external language resources, such as text corpora, to derive the distributional properties of word usage. In this study, we propose that internal language models, that are more closely aligned to the mental representations of words, can be used to derive new theoretical questions regarding the structure of the mental lexicon. A comparison with internal models also puts into perspective a number of assumptions underlying recently proposed distributional text-based models could provide important insights into cognitive science, including linguistics and artificial intelligence. We focus on wordembedding models which have been proposed to lear..