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The cross-linguistic performance of word segmentation models over time

Published online by Cambridge University Press:  11 October 2019

Andrew CAINES ,
Emma ALTMANN-RICHER  and
Paula BUTTERY
Andrew CAINES*
Affiliation:
Department of Computer Science & Technology, University of Cambridge, Cambridge, UK
Emma ALTMANN-RICHER
Affiliation:
Faculty of Modern & Medieval Languages, University of Cambridge, Cambridge, UK
Paula BUTTERY
Affiliation:
Department of Computer Science & Technology, University of Cambridge, Cambridge, UK
*
*Corresponding author: Department of Computer Science & Technology, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. E-mail: andrew.caines@cl.cam.ac.uk
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Abstract

We select three word segmentation models with psycholinguistic foundations – transitional probabilities, the diphone-based segmenter, and PUDDLE – which track phoneme co-occurrence and positional frequencies in input strings, and in the case of PUDDLE build lexical and diphone inventories. The models are evaluated on caregiver utterances in 132 CHILDES corpora representing 28 languages and 11.9 m words. PUDDLE shows the best performance overall, albeit with wide cross-linguistic variation. We explore the reasons for this variation, fitting regression models to performance scores with linguistic properties which capture lexico-phonological characteristics of the input: word length, utterance length, diversity in the lexicon, the frequency of one-word utterances, the regularity of phoneme patterns at word boundaries, and the distribution of diphones in each language. These properties together explain four-tenths of the observed variation in segmentation performance, a strong outcome and a solid foundation for studying further variables which make the segmentation task difficult.

Keywords

word segmentationCHILDESstatistical learning
Type
Articles
Information
Journal of Child Language , Volume 46 , Issue 6 , November 2019 , pp. 1169 - 1201
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Copyright
Copyright © Cambridge University Press 2019 

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