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...

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