Modeling Bilingual Lexical Processing Through Code-Switching Speech: A Network Science Approach.
bilingual lexicon
clustering coefficient
code-switching speech
community detection
computational linguistics
network science
Journal
Frontiers in psychology
ISSN: 1664-1078
Titre abrégé: Front Psychol
Pays: Switzerland
ID NLM: 101550902
Informations de publication
Date de publication:
2021
2021
Historique:
received:
01
02
2021
accepted:
05
07
2021
entrez:
13
9
2021
pubmed:
14
9
2021
medline:
14
9
2021
Statut:
epublish
Résumé
The study of code-switching (CS) speech has produced a wealth of knowledge in the understanding of bilingual language processing and representation. Here, we approach this issue by using a novel network science approach to map bilingual spontaneous CS speech. In Study 1, we constructed semantic networks on CS speech corpora and conducted community detections to depict the semantic organizations of the bilingual lexicon. The results suggest that the semantic organizations of the two lexicons in CS speech are largely distinct, with a small portion of overlap such that the semantic network community dominated by each language still contains words from the other language. In Study 2, we explored the effect of clustering coefficients on language choice during CS speech, by comparing clustering coefficients of words that were code-switched with their translation equivalents (TEs) in the other language. The results indicate that words where the language is switched have lower clustering coefficients than their TEs in the other language. Taken together, we show that network science is a valuable tool for understanding the overall map of bilingual lexicons as well as the detailed interconnections and organizations between the two languages.
Identifiants
pubmed: 34512435
doi: 10.3389/fpsyg.2021.662409
pmc: PMC8423917
doi:
Types de publication
Journal Article
Langues
eng
Pagination
662409Commentaires et corrections
Type : ErratumIn
Informations de copyright
Copyright © 2021 Xu, Markowska, Chodorow and Li.
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