Senseval automatic labeling of semantic roles using Maximum Entropy models.

As a task in SensEval-3, Automatic Labeling of Semantic Roles is to identify frame elements within a sentence and tag them with appropriate semantic roles given a sentence, a target word and its frame. We apply Maximum Entropy classification with feature sets of syntactic patterns from parse trees and officially attain 80.2% precision and 65.4% recall. When the frame element boundaries are given, the system performs 86.7% precision and 85.8% recall.