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This paper reports on experiments in classifying the semantic role annotations assigned 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 features. 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 preposition. When using a single classifier for all of the prepositions together, the combined approach yields a significant gain at 85.8% accuracy versus 81.3% for word-only collocations. For FrameNet, the combined use of both collocation types achieves better performance for the individual classifiers: 70.3% versus 68.5%. However, classification using a single classifier is not effective due to confusion among the fine-grained roles.

Preposition semantic classification via Penn Treebank and FrameNet