A Progressive Feature Selection Algorithm for Ultra Large Feature Spaces

Recent developments in statistical modeling of various linguistic phenomena have shown that additional features give consistent performance improvements. Quite often, improvements are limited by the number of features a system is able to explore. This paper describes a novel progressive training algorithm that selects features from virtually unlimited feature spaces for conditional maximum entropy (CME) modeling. Experimental results in edit region identification demonstrate the benefits of the progressive feature selection (PFS) algorithm: the PFS algorithm maintains the same accuracy performance as previous CME feature selection algorithms (e.g., Zhou et al., 2003) when the same feature spaces are used. When additional features and their combinations are used, the PFS gives 17.66% relative improvement over the previously reported best result in edit region identification on Switchboard corpus (Kahn et al., 2005), which leads to a 20% relative error reduction in parsing the Switchboard corpus when gold edits are used as the upper bound.