The Development of Abstract Concepts in Children’s Early Lexical Networks

Abdellah Fourtassi, Isaac Scheinfeld, Michael Frank


Abstract
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.
Anthology ID:
W19-2914
Volume:
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
CMCL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
129–133
Language:
URL:
https://aclanthology.org/W19-2914
DOI:
10.18653/v1/W19-2914
Bibkey:
Cite (ACL):
Abdellah Fourtassi, Isaac Scheinfeld, and Michael Frank. 2019. The Development of Abstract Concepts in Children’s Early Lexical Networks. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 129–133, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
The Development of Abstract Concepts in Children’s Early Lexical Networks (Fourtassi et al., CMCL 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2914.pdf