An analysis of open information extraction based on semantic role labeling

Open Information Extraction extracts relations from text without requiring a pre-specified domain or vocabulary. While existing techniques have used only shallow syntactic features, we investigate the use of semantic role labeling techniques for the task of Open IE. Semantic role labeling (SRL) and Open IE, although developed mostly in isolation, are quite related. We compare SRLbased open extractors, which perform computationally expensive, deep syntactic analysis, with TextRunner, an open extractor, which uses shallow syntactic analysis but is able to analyze many more sentences in a fixed amount of time and thus exploit corpus-level statistics. Our evaluation answers questions regarding these systems, including, can SRL extractors, which are trained on PropBank, cope with heterogeneous text found on the Web? Which extractor attains better precision, recall, f-measure, or running time? How does extractor performance vary for binary, n-ary and nested relations? How much do we gain ...

• Type: Journal
• Year: 2011
• Where: KCAP
• Authors: Janara Christensen, Mausam, Stephen Soderland, Oren Etzioni
• Keywords: Evaluation Answers, Information Technology, KCAP 2011, Level Statistics, Shallow Syntactic Analysis