Modeling Human Performance in Statistical Word Segmentation

by : Michael C. Frank, et all

2007

Abstract

What mechanisms support the ability of human infants, adults, and other primates to identify words from fluent speech using distributional regularities? In order to better characterize this ability, we collected data from adults in an artificial language segmentation task similar to Saffran, Newport, and Aslin (1996) in which the length of sentences was systematically varied between groups of participants. We then compared the fit of a variety of computational models—
including simple statistical models of transitional probability and mutual information, a clustering model based on mutual information by Swingley (2005), PARSER (Perruchet & Vintner, 1998), and a Bayesian model. We found that while all models were able to successfully complete the task, fit to the human data varied considerably, with the Bayesian model achieving the highest correlation with our results.
Keywords: Statistical learning; word segmentation; language acquisition; Bayesian modeling.

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Artikel Lengkap dikompilasi oleh/ HUBUNGI :

Kanaidi, SE., M.Si  (Penulis “Buku Dasar-Dasar PERIKLANAN”, Service Quality and Motivation Trainer, Dosen Marketing Management,  Praktisi Bisnis)

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