Paper: Analyzing Models For Semantic Role Assignment Using Confusability

ACL ID H05-1084
Title Analyzing Models For Semantic Role Assignment Using Confusability
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

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 de- fined 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 fea- tures approximate a description of gram- matical functions, and that semantic fea- tures provide an independent second view on the data.