Paper: Preposition Semantic Classification Via Penn Treebank And FrameNet

ACL ID W03-0411
Title Preposition Semantic Classification Via Penn Treebank And FrameNet
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2003
Authors

This paper reports on experiments in clas- sifying the semantic role annotations as- signed to prepositional phrases in both the PENN TREEBANK and FRAMENET.In both cases, experiments are done to see how the prepositions can be classified given the dataset’s role inventory, using standard word-sense disambiguation fea- tures. In addition to using traditional word collocations, the experiments incorporate class-based collocations in the form of WordNet hypernyms. For Treebank, the word collocations achieve slightly better performance: 78.5% versus 77.4% when separate classifiers are used per preposi- tion. When using a single classifier for all of the prepositions together, the com- bined approach yields a significant gain at 85.8% accuracy versus 81.3% for word- only collocations. For Fra...