We describe a novel approach for inducing unsupervised part-of-speech taggers for languages that have no labeled training data, but have translated text in a resource-rich language. Our method does not assume any knowledge about the target language (in particular no tagging dictionary is assumed), making it applicable to a wide array of resource-poor languages. We use graph-based label propagation for cross-lingual knowledge transfer and use the projected labels as features in an unsupervised model (BergKirkpatrick et al., 2010). Across eight European languages, our approach results in an average absolute improvement of 10.4% over a state-of-the-art baseline, and 16.7% over vanilla hidden Markov models induced with the Expectation Maximization algorithm.
REF also used parallel data but additionally exploited graph-based label propagation to expand the coverage of labelled tokens.
Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Computer Science