Paper: Learning Verb Argument Structure From Minimally Annotated Corpora

ACL ID C02-1040
Title Learning Verb Argument Structure From Minimally Annotated Corpora
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2002
Authors

In this paper we investigate the task of automatically identifying the correct argument structure for a set of verbs. The argument structure of a verb allows us to predict the relationship between the syntac- tic arguments of a verb and their role in the under- lying lexical semantics of the verb. Following the method described in (Merlo and Stevenson, 2001), we exploit the distributions of some selected fea- tures from the local context of a verb. These fea- tures were extracted from a 23M word WSJ cor- pus based on part-of-speech tags and phrasal chunks alone. We constructed several decision tree classi- fiers trained on this data. The best performing clas- sifier achieved an error rate of 33.4%. This work shows that a subcategorization frame (SF) learning algorithm previously applied to...