Relation extraction is the task of recog- nizing semantic relations among entities. Given a particular sentence supervised ap- proaches to Relation Extraction employed feature or kernel functions which usu- ally have a single sentence in their scope. The overall aim of this paper is to pro- pose methods for using knowledge and re- sources that are external to the target sen- tence, as a way to improve relation ex- traction. We demonstrate this by exploit- ing background knowledge such as rela- tionships among the target relations, as well as by considering how target rela- tions relate to some existing knowledge resources. Our methods are general and we suggest that some of them could be ap- plied to other NLP tasks.
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