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

ACL ID D09-1072
Title A Simple Unsupervised Learner for POS Disambiguation Rules Given Only a Minimal Lexicon
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

We propose a new model for unsupervised POS tagging based on linguistic distinc- tionsbetweenopenandclosed-classitems. Exploiting notions from current linguis- tic theory, the system uses far less infor- mation than previous systems, far simpler computational methods, and far sparser descriptions in learning contexts. By ap- plying simple language acquisition tech- niques 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 sys- tem achieves a 20% error reduction for POS tagging over state-of-the-art unsuper- vised systems tested under the same con- ditions, and achieves comparable accuracy when trained with much less prior infor- mation.