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Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children(More)
How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms(More)
We present a probabilistic incremental model of early word learning. The model acquires the meaning of words from exposure to word usages in sentences, paired with appropriate semantic representations, in the presence of referential uncertainty. A distinct property of our model is that it continually revises its learned knowledge of a word's meaning, but(More)
Children learn a robust representation of lexical categories at a young age. We propose an incremental model of this process which efficiently groups words into lexical categories based on their local context using an information-theoretic criterion. We train our model on a corpus of child-directed speech from CHILDES and show that the model learns a(More)
How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms(More)
Higher frequency has been shown to have a positive effect on the acquisition of words and other linguistic items in children. An important question that needs to be answered then is how children learn low frequency items. In this study, we investigate the acquisition of meanings for low frequency words through computational modeling. We suggest that for(More)
When looking for the referents of novel nouns, adults and young children are sensitive to cross-situational statistics (Yu and Smith, 2007; Smith and Yu, 2008). In addition, the linguistic context that a word appears in has been shown to act as a powerful attention mechanism for guiding sentence processing and word learning (Landau and Gleitman, 1985;(More)