Publication at Faculty of Mathematics and Physics |
2013
Abstract
We present an unsupervised approach to part-of-speech tagging based on projections of tags in a word-aligned bilingual parallel corpus. In contrast to the existing state-of-the-art approach of Das and Petrov, we have developed a substantially simpler method by automatically identifying ""good"" training sentences from the parallel corpus and applying self-training.
In experimental results on eight languages, our method achieves state-of-the-art results.