CMILLS: Adapting Semantic Role Labeling Features to Dependency Parsing
نویسندگان
چکیده
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.
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