Weakly Supervised Training for Parsing Mandarin Broadcast Transcripts

Abstract

We present a systematic investigation of applying weakly supervised co-training approaches to improve parsing performance for parsing Mandarin broadcast news (BN) and broadcast conversation (BC) transcripts, by iteratively retraining two competitive Chinese parsers from a small set of treebanked data and a large set of unlabeled data. We compare co-training to self-training, and our results show that performance using co-training is significantly better than with self-training and both co-training and self-training with a small seed labeled corpus can improve parsing accuracy significantly over training on the mismatching newswire treebank.