@inproceedings{gaume-etal-2019-toward,
title = "Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics",
author = "Gaume, Bruno and
Mai Ho-Dac, Lydia and
Tanguy, Ludovic and
Fabre, C{\'e}cile and
Pierrejean, B{\'e}n{\'e}dicte and
Hathout, Nabil and
Farinas, J{\'e}r{\^o}me and
Pinquier, Julien and
Danet, Lola and
P{\'e}ran, Patrice and
De Boissezon, Xavier and
Jucla, M{\'e}lanie",
editor = "Chersoni, Emmanuele and
Jacobs, Cassandra and
Lenci, Alessandro and
Linzen, Tal and
Pr{\'e}vot, Laurent and
Santus, Enrico",
booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2908",
doi = "10.18653/v1/W19-2908",
pages = "71--76",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics
%A Gaume, Bruno
%A Mai Ho-Dac, Lydia
%A Tanguy, Ludovic
%A Fabre, Cécile
%A Pierrejean, Bénédicte
%A Hathout, Nabil
%A Farinas, Jérôme
%A Pinquier, Julien
%A Danet, Lola
%A Péran, Patrice
%A De Boissezon, Xavier
%A Jucla, Mélanie
%Y Chersoni, Emmanuele
%Y Jacobs, Cassandra
%Y Lenci, Alessandro
%Y Linzen, Tal
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F gaume-etal-2019-toward
%X 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.
%R 10.18653/v1/W19-2908
%U https://aclanthology.org/W19-2908
%U https://doi.org/10.18653/v1/W19-2908
%P 71-76
Markdown (Informal)
[Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics](https://aclanthology.org/W19-2908) (Gaume et al., CMCL 2019)
ACL
- 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, and Mélanie Jucla. 2019. Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 71–76, Minneapolis, Minnesota. Association for Computational Linguistics.