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.
REF presented a method for guessing POS tags of pre-segmented unknown words that took into consideration all the occurrences of each unknown word in a document.
Guessing Parts-Of-Speech Of Unknown Words Using Global Information
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics
Computer Science