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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
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Griffiths, Thomas L.; Steyvers, Mark; Tenenbaum, Joshua B. – Psychological Review, 2007
Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem…
Descriptors: Language Processing, Information Retrieval, Fundamental Concepts, Syntax