Diarmuid Ó Séaghdha
Learning syntactic verb frames using graphical models
Tom Lippincott, Anna Korhonen and Diarmuid Ó Séaghdha
We present a novel approach for building verb subcategorization lexicons using a simple graphical model. In contrast to previous methods, we show how the model can be trained without parsed input or a predefined subcategorization frame inventory. Our method outperforms the state-of-the-art on a verb clustering task, and is easily trained on arbitrary domains. This quantitative evaluation is complemented 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.
@InProceedings{Lippincott:EtAl:12,
author = {Tom Lippincott and Anna Korhonen and Diarmuid {\'O S\'eaghdha}},
title = {Learning syntactic verb frames using graphical models},
booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012)},
year = 2012,
address = {Jeju, Korea}
}