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Stevens, Jon Scott; Gleitman, Lila R.; Trueswell, John C.; Yang, Charles – Cognitive Science, 2017
We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed "Pursuit," uses an associative…
Descriptors: Semantics, Associative Learning, Probability, Computational Linguistics
Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco – Cognitive Science, 2016
Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…
Descriptors: Orthographic Symbols, Neurological Organization, Models, Probability
Mirman, Daniel; Estes, Katharine Graf; Magnuson, James S. – Infancy, 2010
Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network…
Descriptors: Infants, Probability, Language Acquisition, Vocabulary Development
Heintz, Ilana – ProQuest LLC, 2010
The goal of this dissertation is to introduce a method for deriving morphemes from Arabic words using stem patterns, a feature of Arabic morphology. The motivations are three-fold: modeling with morphemes rather than words should help address the out-of-vocabulary problem; working with stem patterns should prove to be a cross-dialectally valid…
Descriptors: Semitic Languages, Dialects, Vowels, Morphemes
Meara, Paul – International Journal of English Studies, 2007
This paper describes a set of simulations which explore the way different features of lexical organisation affect the probability of finding a pair of associated words in a set of five randomly selected words. The simulation is equivalent to giving Ss a set of five words and asking if they can identify a pair of associated words among them. The…
Descriptors: Second Language Learning, Associative Learning, Vocabulary Development, Simulation
Maekawa, Junko; Storkel, Holly L. – Journal of Child Language, 2006
The current study attempts to differentiate effects of phonotactic probability (i.e. the likelihood of occurrence of a sound sequence), neighbourhood density (i.e. the number of phonologically similar words), word frequency, and word length on expressive vocabulary development by young children. Naturalistic conversational samples for three…
Descriptors: Young Children, Vocabulary Development, Word Frequency, Probability
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers