A Simple Unsupervised Learner for POS Disambiguation Rules Given Only a Minimal Lexicon

We propose a new model for unsupervised POS tagging based on linguistic distinctions between open and closed-class items. Exploiting notions from current linguistic theory, the system uses far less information than previous systems, far simpler computational methods, and far sparser descriptions in learning contexts. By applying simple language acquisition techniques based on counting, the system is given the closed-class lexicon, acquires a large open-class lexicon and then acquires disambiguation rules for both. This system achieves a 20% error reduction for POS tagging over state-of-the-art unsupervised systems tested under the same conditions, and achieves comparable accuracy when trained with much less prior information.