Guessing parts-of-speech of unknown words using global information

Abstract

In this paper, we present a method for guessing POS tags of unknown words using local and global information. Although many existing methods use only local information (i.e. limited window size or intra-sentential features), global information (extra-sentential features) provides valuable clues for predicting POS tags of unknown words. We propose a probabilistic model for POS guessing of unknown words using global information as well as local information, and estimate its parameters using Gibbs sampling. We also attempt to apply the model to semisupervised learning, and conduct experiments on multiple corpora. © 2006 Association for Computational Linguistics.