Unsupervised part of speech tagging using unambiguous substitutes from a statistical language model

Abstract

We show that unsupervised part of speech tagging performance can be significantly improved using likely substitutes for target words given by a statistical language model. We choose unambiguous substitutes for each occurrence of an ambiguous target word based on its context. The part of speech tags for the unambiguous substitutes are then used to filter the entry for the target word in the word-tag dictionary. A standard HMM model trained using the filtered dictionary achieves 92.25% accuracy on a standard 24,000 word corpus.