A Maximum Entropy Approach to FrameNet Tagging

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.

• Type: Conference
• Year: 2003
• Where: NAACL
• Authors: Michael Fleischman, Eduard H. Hovy
• Keywords: Human Generated Syntactic, Maximum Entropy Approach, NAACL 2003, NAACL 2007, Syntactic Pattern