A maximum entropy approach to FrameNet tagging

Authors:
Michael Fleischman;Eduard Hovy
Affiliations:
USC Information Science Institute, Marina del Rey, CA;USC Information Science Institute, Marina del Rey, CA
Venue:
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Year:
2003

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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 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.