Abdellah Fourtassi


2020

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Word Co-occurrence in Child-directed Speech Predicts Children’s Free Word Associations
Abdellah Fourtassi
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

The free association task has been very influential both in cognitive science and in computational linguistics. However, little research has been done to study how free associations develop in childhood. The current work focuses on the developmental hypothesis according to which free word associations emerge by mirroring the co-occurrence distribution of children’s linguistic environment. I trained a distributional semantic model on a large corpus of child language and I tested if it could predict children’s responses. The results largely supported the hypothesis: Co-occurrence-based similarity was a strong predictor of children’s associative behavior even controlling for other possible predictors such as phonological similarity, word frequency, and word length. I discuss the findings in the light of theories of conceptual development.

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Development of Multi-level Linguistic Alignment in Child-adult Conversations
Thomas Misiek | Benoit Favre | Abdellah Fourtassi
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Interactive alignment is a major mechanism of linguistic coordination. Here we study the way this mechanism emerges in development across the lexical, syntactic, and conceptual levels. We leverage NLP tools to analyze a large-scale corpus of child-adult conversations between 2 and 5 years old. We found that, across development, children align consistently to adults above chance and that adults align consistently more to children than vice versa (even controlling for language production abilities). Besides these consistencies, we found a diversity of developmental trajectories across linguistic levels. These corpus-based findings provide strong support for an early onset of multi-level linguistic alignment in children and invites new experimental work.

2019

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The Development of Abstract Concepts in Children’s Early Lexical Networks
Abdellah Fourtassi | Isaac Scheinfeld | Michael Frank
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

How do children learn abstract concepts such as animal vs. artifact? Previous research has suggested that such concepts can partly be derived using cues from the language children hear around them. Following this suggestion, we propose a model where we represent the children’ developing lexicon as an evolving network. The nodes of this network are based on vocabulary knowledge as reported by parents, and the edges between pairs of nodes are based on the probability of their co-occurrence in a corpus of child-directed speech. We found that several abstract categories can be identified as the dense regions in such networks. In addition, our simulations suggest that these categories develop simultaneously, rather than sequentially, thanks to the children’s word learning trajectory which favors the exploration of the global conceptual space.

2014

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Exploring the Relative Role of Bottom-up and Top-down Information in Phoneme Learning
Abdellah Fourtassi | Thomas Schatz | Balakrishnan Varadarajan | Emmanuel Dupoux
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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A Rudimentary Lexicon and Semantics Help Bootstrap Phoneme Acquisition
Abdellah Fourtassi | Emmanuel Dupoux
Proceedings of the Eighteenth Conference on Computational Natural Language Learning

2013

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Why is English so easy to segment?
Abdellah Fourtassi | Benjamin Börschinger | Mark Johnson | Emmanuel Dupoux
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)

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A corpus-based evaluation method for Distributional Semantic Models
Abdellah Fourtassi | Emmanuel Dupoux
51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop