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1
The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
Lavi-Rotbain, Ori; Arnon, Inbal. - : PsychArchives, 2020
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2
The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
Lavi-Rotbain, Ori; Arnon, Inbal. - : PsychArchives, 2020
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3
Not all words are equally acquired: transitional probabilities and instructions affect the electrophysiological correlates of statistical learning
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4
Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus
In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-02274050 ; Open Mind, MIT Press, 2019, 3, pp.13-22. ⟨10.1162/opmi_a_00022⟩ (2019)
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5
Statistical Learning in a Bilingual Environment ...
Tsui, Sin Mei. - : Université d'Ottawa / University of Ottawa, 2018
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6
Statistical Learning in a Bilingual Environment
Tsui, Sin Mei. - : Université d'Ottawa / University of Ottawa, 2018
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7
Understanding Patterns in Infant-Directed Speech in Context: An Investigation of Statistical Cues to Word Boundaries
Hartman, Rose. - : University of Oregon, 2017
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8
The neural correlates of statistical learning in a word segmentation task: An fMRI study
In: ISSN: 0093-934X ; Brain and Language, Vol. 127, No 1 (2013) pp. 46-54 (2013)
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9
Verbs are LookING Good in Language Acquisition
In: http://141.14.165.6/CogSci09/papers/584/paper584.pdf
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10
Modeling Human Performance in Statistical Word Segmentation
In: http://cocosci.berkeley.edu/tom/papers/wordseg2.pdf
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.
Keyword: Bayesian; language acquisition; Statistical learning; word segmentation
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.6889
http://cocosci.berkeley.edu/tom/papers/wordseg2.pdf
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11
Modeling Human Performance in Statistical Word Segmentation
In: http://www.stanford.edu/~sgwater/papers/cogsci07_final.pdf
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