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ACL
2009

Unsupervised Argument Identification for Semantic Role Labeling

8 years 11 months ago
Unsupervised Argument Identification for Semantic Role Labeling
The task of Semantic Role Labeling (SRL) is often divided into two sub-tasks: verb argument identification, and argument classification. Current SRL algorithms show lower results on the identification sub-task. Moreover, most SRL algorithms are supervised, relying on large amounts of manually created data. In this paper we present an unsupervised algorithm 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 collocation statistics. We evaluate our algorithm on PropBank10, achieving a precision of 56%, as opposed to 47% of a strong baseline. We also obtain an 8% increase in precision for a Spanish corpus. This is the first paper that tackles unsupervised verb argument identification without using manually encoded rules or extensive lexical or syntactic resources.
Omri Abend, Roi Reichart, Ari Rappoport
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Omri Abend, Roi Reichart, Ari Rappoport
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