Learning verb classes in an incremental model

The ability of children to generalize over the linguistic input they receive is key to acquiring productive knowledge of verbs. Such generalizations help children extend their learned knowledge of constructions to a novel verb, and use it appropriately in syntactic patterns previously unobserved for that verb—a key factor in language productivity. Computational models can help shed light on the gradual development of more abstract knowledge during verb acquisition. We present an incremental Bayesian model that simultaneously and incrementally learns argument structure constructions and verb classes given nat-uralistic language input. We show how the distributional properties in the input lan-guage influence the formation of general-izations over the constructions and classes.