Departmental Papers (CIS)

Date of this Version

7-1-2011

Document Type

Journal Article

Comments

Graca, J., Ganchev, K., Pereira, F., Coheur, L., & Taskar, B. (2011). Controlling Complexity in Part-of-Speech Induction. Journal of Artificial Intelligence Research, 41, (527-551). doi: 10.1613/jair.3348

© 2011 Advancement of Artificial Intelligence

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.

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Date Posted: 15 June 2012

This document has been peer reviewed.