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Semantic Role Labeling of NomBank: A Maximum Entropy Approach

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Abstract

This paper describes our attempt at NomBank-based automatic Semantic Role Labeling (SRL). NomBank is a project at New York University to annotate the argument structures for common nouns in the Penn Treebank II corpus. We treat the NomBank SRL task as a classification problem and explore the possibility of adapting features previously shown useful in PropBank-based SRL systems. Various NomBank-specific features are explored. On test section 23, our best system achieves F1 score of 72.73 (69.14) when correct (automatic) syntactic parse trees are used. To our knowledge, this is the first reported automatic NomBank SRL system

Topics: made possible by the availability of Prop- Bank (Kingsbury and Palmer, 2003, Palmer et
Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.1127
Provided by: CiteSeerX
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