Paper: Multi-Class Confidence Weighted Algorithms

ACL ID D09-1052
Title Multi-Class Confidence Weighted Algorithms
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

The recently introduced online confidence-weighted (CW) learning algorithm for binary classification per- forms well on many binary NLP tasks. However, for multi-class problems CW learning updates and inference cannot be computed analytically or solved as convex optimization problems as they are in the binary case. We derive learning algorithms for the multi-class CW setting and provide extensive evaluation using nine NLP datasets, including three derived from the recently released New York Times corpus. Our best algorithm out- performs state-of-the-art online and batch methods on eight of the nine tasks. We also show that the confidence information maintained during learning yields useful probabilistic information at test time.