Learning words and their meanings from unsegmented child-directed speech

Authors:
Bevan K. Jones;Mark Johnson;Michael C. Frank
Affiliations:
Brown University, Providence, RI;Brown University, Providence, RI;Massachusetts Institute of Technology, Cambridge, MA
Venue:
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2010

Citing 4
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Abstract

Most work on language acquisition treats word segmentation---the identification of linguistic segments from continuous speech---and word learning---the mapping of those segments to meanings---as separate problems. These two abilities develop in parallel, however, raising the question of whether they might interact. To explore the question, we present a new Bayesian segmentation model that incorporates aspects of word learning and compare it to a model that ignores word meanings. The model that learns word meanings proposes more adult-like segmentations for the meaning-bearing words. This result suggests that the non-linguistic context may supply important information for learning word segmentations as well as word meanings.