Paper: Learning Syntactic Verb Frames using Graphical Models

ACL ID P12-1044
Title Learning Syntactic Verb Frames using Graphical Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012

We present a novel approach for building verb subcategorization lexicons using a simple graphical model. In contrast to previous meth- ods, we show how the model can be trained without parsed input or a predefined subcate- gorization frame inventory. Our method out- performs the state-of-the-art on a verb clus- tering task, and is easily trained on arbitrary domains. This quantitative evaluation is com- plemented by a qualitative discussion of verbs and their frames. We discuss the advantages of graphical models for this task, in particular the ease of integrating semantic information about verbs and arguments in a principled fashion. We conclude with future work to augment the approach.