Paper: Unsupervised Argument Identification for Semantic Role Labeling

ACL ID P09-1004
Title Unsupervised Argument Identification for Semantic Role Labeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009

The task of Semantic Role Labeling (SRL) is often divided into two sub-tasks: verb argument identification, and argu- ment classification. Current SRL algo- rithms show lower results on the identifi- cation sub-task. Moreover, most SRL al- gorithms are supervised, relying on large amounts of manually created data. In this paper we present an unsupervised al- gorithm for identifying verb arguments, where the only type of annotation required is POS tagging. The algorithm makes use of a fully unsupervised syntactic parser, using its output in order to detect clauses and gather candidate argument colloca- tion statistics. We evaluate our algorithm on PropBank10, achieving a precision of 56%, as opposed to 47% of a strong base- line. We also obtain an 8% increase in precision for a Spanish corp...