Automatic analysis of word association data from the Evolex psycholinguistic tasks using computational lexical semantic similarity measures

Abstract : This paper is the fruit of a multidisciplinary project gathering researchers in Psycholinguistics, Neuropsychology, Computer Science, Natural Language Processing and Linguistics. It proposes a new data-based inductive method for automatically characterising the relation between pairs of words collected in psycholinguistics experiments on lexical access. This method takes advantage of four complementary computational measures of semantic similarity. We compare these techniques by assessing their correlation with a manual categorisation of 559 distinct word pairs, and with the distribution of data produced by 30 test subjects. 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.
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Bruno Gaume, Ludovic Tanguy, Cécile Fabre, Lydia-Mai Ho-Dac, Bénédicte Pierrejean, et al.. Automatic analysis of word association data from the Evolex psycholinguistic tasks using computational lexical semantic similarity measures. 13th International Workshop on Natural Language Processing and Cognitive Science (NLPCS), Sep 2018, Krakow, Poland. ⟨hal-01881336⟩

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