Semi-Supervised Learning for Part-of-Speech Tagging of Mandarin Transcribed Speech

In this paper, we investigate bootstrapping part-of-speech (POS) taggers for Mandarin broadcast news (BN) transcripts using co-training, by iteratively retraining two competitive POS taggers from a small set of labeled training data and a large set of unlabeled data. We compare co-training with self-training and our results show that the performance using co-training is significantly better than that from self-training and these semi-supervised learning methods significantly improve tagging accuracy over training only on the small labeled seed corpus. We also investigate a variety of example selection approaches for co-training and find that the computationally expensive, agreement-based selection approach and a more efficient selection approach based on maximizing training utility produce comparable tagging performance from resulting POS taggers. By applying co-training, we are able to build effective POS taggers for Mandarin transcribed speech with the tagging accuracy comparable to that obtained on newswire text.