Paper: Weakly Supervised Natural Language Learning Without Redundant Views

ACL ID N03-1023
Title Weakly Supervised Natural Language Learning Without Redundant Views
Venue Human Language Technologies
Session Main Conference
Year 2003
Authors

We investigate single-view algorithms as an al- ternative to multi-view algorithms for weakly supervised learning for natural language pro- cessing tasks without a natural feature split. In particular, we apply co-training, self-training, and EM to one such task and find that both self- training and FS-EM, a new variation of EM that incorporates feature selection, outperform co- training and are comparatively less sensitive to parameter changes.