Modeling Infant Word Segmentation

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.

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