Paper: Feature Selection For A Rich HPSG Grammar Using Decision Trees

ACL ID W02-2030
Title Feature Selection For A Rich HPSG Grammar Using Decision Trees
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2002
Authors

This paper examines feature selection for log linear models over rich constraint-based grammar (HPSG) representations by building decision trees over fea- tures in corresponding probabilistic context free grammars (PCFGs). We show that single decision trees do not make optimal use of the available in- formation; constructed ensembles of decision trees based on different feature subspaces show signifi- cant performance gains (14% parse selection error reduction). We compare the performance of the learned PCFG grammars and log linear models over the same features.