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2008

Extraction of Entailed Semantic Relations Through Syntax-Based Comma Resolution

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Extraction of Entailed Semantic Relations Through Syntax-Based Comma Resolution
This paper studies textual inference by investigating comma structures, which are highly frequent elements whose major role in the extraction of semantic relations has not been hitherto recognized. We introduce the problem of comma resolution, defined as understanding the role of commas and extracting the relations they imply. We show the importance of the problem using examples from Textual Entailment tasks, and present A Sentence Transformation Rule Learner (ASTRL), a machine learning algorithm that uses a syntactic analysis of the sentence to learn sentence transformation rules that can then be used to extract relations. We have manually annotated a corpus identifying comma structures and relations they entail and experimented with both gold standard parses and parses created by a leading statistical parser, obtaining F-scores of 80.2% and 70.4% respectively.
Vivek Srikumar, Roi Reichart, Mark Sammons, Ari Ra
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ACL
Authors Vivek Srikumar, Roi Reichart, Mark Sammons, Ari Rappoport, Dan Roth
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