We investigate single-view algorithms as an alternative to multi-view algorithms for weakly supervised learning for natural language processing tasks without a natural feature split. In particular, we apply co-training, self-training, and EM to one such task and find that both selftraining and FS-EM, a new variation of EM that incorporates feature selection, outperform cotraining and are comparatively less sensitive to parameter changes.
As an alternative to co-training, REF use EM to augment a supervised coreference system with unlabeled data.
Weakly Supervised Natural Language Learning Without Redundant Views
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science