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.
Cite
CITATION STYLE
Fleischman, M., & Hovy, E. (2003). A maximum entropy approach to FrameNet tagging. In Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics - Short Papers, HLT-NAACL 2003 (pp. 22–24). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1073483.1073491
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