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• W2131650863 abstract "We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text. The standard maximum-likelihood hidden Markov model for this task performs poorly, because of its weak inductive bias and large model capacity. We address this problem by refining the model and modifying the learning objective to control its capacity via parametric and non-parametric constraints. Our approach enforces word-category association sparsity, adds morphological and orthographic features, and eliminates hard-to-estimate parameters for rare words. We develop an efficient learning algorithm that is not much more computationally intensive than standard training. We also provide an open-source implementation of the algorithm. Our experiments on five diverse languages (Bulgarian, Danish, English, Portuguese, Spanish) achieve significant improvements compared with previous methods for the same task." @default.
• W2131650863 created "2016-06-24" @default.
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• W2131650863 date "2011-05-01" @default.
• W2131650863 modified "2023-10-16" @default.
• W2131650863 title "Controlling complexity in part-of-speech induction" @default.
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• W2131650863 doi "https://doi.org/10.5555/2051237.2051253" @default.
• W2131650863 hasPublicationYear "2011" @default.
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