CMILLS: Adapting Semantic Role Labeling Features to Dependency Parsing
Chad Mills, Gina‐Anne Levow · 2015
We describe a system for semantic role labeling adapted to a dependency parsing framework.Verb arguments are predicted over nodes in a dependency parse tree instead of nodes in a phrase-structure parse tree.Our system participated in SemEval-2015 shared Task 15, Subtask 1: CPA parsing and achieved an Fscore of 0.516.We adapted features from prior semantic role labeling work to the dependency parsing paradigm, using a series of supervised classifiers to identify arguments of a verb and then assigning syntactic and semantic labels.We found that careful feature selection had a major impact on system performance.However, sparse training data still led rule-based systems like the baseline to be more effective than learning-based approaches.