A Probabilistic Computational Model of Cross-Situational Word Learning
Article first published online: 13 MAY 2010
Copyright © 2010 Cognitive Science Society, Inc.
Volume 34, Issue 6, pages 1017–1063, August 2010
How to Cite
Fazly, A., Alishahi, A. and Stevenson, S. (2010), A Probabilistic Computational Model of Cross-Situational Word Learning. Cognitive Science, 34: 1017–1063. doi: 10.1111/j.1551-6709.2010.01104.x
- Issue published online: 3 AUG 2010
- Article first published online: 13 MAY 2010
- Received 14 November 2008; received in revised form 22 December 2009; accepted 04 January 2010
- Word learning;
- Child language acqusition;
- Computational modeling;
- Cross-situational learning
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 in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.