Lydia Mai Ho-Dac


2019

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Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics
Bruno Gaume | Lydia Mai Ho-Dac | Ludovic Tanguy | Cécile Fabre | Bénédicte Pierrejean | Nabil Hathout | Jérôme Farinas | Julien Pinquier | Lola Danet | Patrice Péran | Xavier De Boissezon | Mélanie Jucla
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

This paper presents the first results of a multidisciplinary project, the “Evolex” project, gathering researchers in Psycholinguistics, Neuropsychology, Computer Science, Natural Language Processing and Linguistics. The Evolex project aims at proposing a new data-based inductive method for automatically characterising the relation between pairs of french words collected in psycholinguistics experiments on lexical access. This method takes advantage of several complementary computational measures of semantic similarity. We show that some measures are more correlated than others with the frequency of lexical associations, and that they also differ in the way they capture different semantic relations. This allows us to consider building a multidimensional lexical similarity to automate the classification of lexical associations.