Paper: A Maximum Entropy Approach To FrameNet Tagging

ACL ID N03-2008
Title A Maximum Entropy Approach To FrameNet Tagging
Venue Human Language Technologies
Session Short Paper
Year 2003

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 Viterbi search to find the highest probability tag sequence for a given sentence. Further we examine the use of syntactic pattern based re-ranking to further increase performance. We analyze our strategy using both extracted and human generated syntactic features. Experiments indicate 85.7% accuracy using human annotations on a held out test set.