Paper: A Progressive Feature Selection Algorithm For Ultra Large Feature Spaces

ACL ID P06-1071
Title A Progressive Feature Selection Algorithm For Ultra Large Feature Spaces
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

Recent developments in statistical modeling of various linguistic phenomena have shown that additional features give consistent per- formance improvements. Quite often, im- provements are limited by the number of fea- tures a system is able to explore. This paper describes a novel progressive training algo- rithm that selects features from virtually unlimited feature spaces for conditional maximum entropy (CME) modeling. Experi- mental results in edit region identification demonstrate the benefits of the progressive feature selection (PFS) algorithm: the PFS algorithm maintains the same accuracy per- formance as previous CME feature selection algorithms (e.g. , Zhou et al. , 2003) when the same feature spaces are used. When addi- tional features and their combinations are used, the PFS giv...