Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
An Efficient, Probabilistically Sound Algorithm for Segmentation andWord Discovery
Machine Learning - Special issue on natural language learning
Acoustic characteristics of lexical stress in continuous telephone speech
Speech Communication
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Computational Linguistics
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EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
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While many computational models have been created to explore how children might learn to segment words, the focus has largely been on achieving higher levels of performance and exploring cues suggested by artificial learning experiments. We propose a broader focus that includes designing models that display properties of infants' performance as they begin to segment words. We develop an efficient bootstrapping online learner with this focus in mind, and evaluate it on child-directed speech. In addition to attaining a high level of performance, this model predicts the error patterns seen in infants learning to segment words.