CLASSIC-Utterance-Boundary: a chunking-based model of early naturalistic word segmentation

Cabiddu, F., Bott, L., Jones, G. ORCID: 0000-0003-3867-9947 and Gambi, C., 2022. CLASSIC-Utterance-Boundary: a chunking-based model of early naturalistic word segmentation. Language Learning. ISSN 0023-8333 (Forthcoming)

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Abstract

Word segmentation is a crucial step in children’s vocabulary learning. While computational models of word segmentation can capture infants’ performance in small-scale artificial tasks, the examination of early word segmentation in naturalistic settings has been limited by the lack of measures that can relate models’ performance to developmental data. Here, we extended CLASSIC (Jones et al., 2021) - a corpus-trained chunking model that can simulate several memory, phonological and vocabulary learning phenomena - to allow it to perform word segmentation using utterance boundary information (henceforth CLASSIC-UB). Further, we compared our model to children on a wide range of new measures, capitalizing on the link between word segmentation and vocabulary learning abilities. We show that the combination of chunking and utterance-boundary information used by CLASSIC-UB allows a better prediction of English-learning children's output vocabulary than other models.

Item Type: Journal article
Alternative Title: CLASSIC-Utterance-Boundary chunking-based model
Publication Title: Language Learning
Creators: Cabiddu, F., Bott, L., Jones, G. and Gambi, C.
Publisher: Wiley
Date: 13 December 2022
ISSN: 0023-8333
Identifiers:
NumberType
10.31234/osf.io/ykzrfDOI
1629413Other
Divisions: Schools > School of Social Sciences
Record created by: Laura Ward
Date Added: 20 Dec 2022 15:31
Last Modified: 02 Feb 2023 16:02
URI: https://irep.ntu.ac.uk/id/eprint/47685

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