Paper: Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections

ACL ID P11-1061
Title Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We describe a novel approach for inducing unsupervised part-of-speech taggers for lan- guages that have no labeled training data, but have translated text in a resource-rich lan- guage. Our method does not assume any knowledge about the target language (in par- ticular 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 knowl- edge transfer and use the projected labels as features in an unsupervised model (Berg- Kirkpatrick et al., 2010). Across eight Eu- ropean 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.