Paper: Feature Lattices for Maximum Entropy Modelling

ACL ID C98-2135
Title Feature Lattices for Maximum Entropy Modelling
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1998

Maximum entropy framework proved to be ex- pressive and powerful for the statistical lan- guage modelling, but it suffers from the com- putational expensiveness of the model build- ing. The iterative scaling algorithm that is used for the parameter estimation is computation- ally expensive while the feature selection pro- cess might require to estimate parameters for many candidate features many times. In this paper we present a novel approach for building maximum entropy models. Our approach uses the feature collocation lattice and builds com- plex candidate features without resorting to it- erative scaling.