An Efficient, Probabilistically Sound Algorithm for Segmentation andWord Discovery
Machine Learning - Special issue on natural language learning
Regular Grammatical Inference from Positive and Negative Samples by Genetic Search: the GIG Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Unsupervised Segmentation of Categorical Time Series into Episodes
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
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Children are facile at both discovering word boundaries and using those words to build higher-level structures in tandem. Current research treats lexical acquisition and grammar induction as two distinct tasks; doing so has led to unreasonable assumptions. State-of-the-art unsupervised results presuppose a perfectly segmented, noise-free lexicon, while largely ignoring how the lexicon is used. This paper combines both tasks in a novel framework for bootstrapping lexical acquisition and grammar induction.