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NAACL
2003

A Maximum Entropy Approach to FrameNet Tagging

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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.
Michael Fleischman, Eduard H. Hovy
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Michael Fleischman, Eduard H. Hovy
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