Maximum Entropy Models for FrameNet Classification

Abstract

The development of FrameNet, a large database of semantically annotated sentences, has primed research into statistical methods for semantic tagging. We advance previous work by adopting a Maximum Entropy approach and by using previous tag information to find the highest probability tag sequence for a given sentence. Further we examine the use of sentence level syntactic pattern features to increase performance. We analyze our strategy on both human annotated and automatically identified frame elements, and compare performance to previous work on identical test data. Experiments indicate a statistically significant improvement (p<0.01) of over 6%.