Paper: Maximum Entropy Models For FrameNet Classification

ACL ID W03-1007
Title Maximum Entropy Models For FrameNet Classification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2003

The development of FrameNet, a large database of semantically annotated sen- tences, has primed research into statistical methods for semantic tagging. We ad- vance previous work by adopting a Maximum Entropy approach and by using previous tag information to find the high- est 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 im- provement (p<0.01) of over 6%.