Probabilistic Models of Verb-Argument Structure
We evaluate probabilistic models of verb argument structure trained on a corpus of verbs and their syntactic arguments. Models designed to represent patterns of verb alternation behavior are compared with generic clustering models in terms of the perplexity assigned to held-out test data. While the specialized models of alternation do not perform as well, closer examination reveals alternation behavior represented implicitly in the generic models.