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We analyze models for semantic role assignment by defining a meta-model that abstracts over features and learning paradigms. This meta-model is based on the concept of role confusability, is defined in information-theoretic terms, and predicts that roles realized by less specific grammatical functions are more difficult to assign. We find that confusability is strongly correlated with the performance of classifiers based on syntactic features, but not for classifiers including semantic features. This indicates that syntactic features approximate a description of grammatical functions, and that semantic features provide an independent second view on the data.

Analyzing models for semantic role assignment using confusability