Small Statistical Models by Random Feature Mixing
Kuzman Ganchev, Mark H. Dredze · 2008
The application of statistical NLP systems to resource constrained devices is limited by the need to maintain parameters for a large number of features and an alphabet mapping features to parameters. We introduce random feature mixing to eliminate alphabet storage and reduce the number of parameters without severely impacting model performance. 1