Exploiting background knowledge for relation extraction

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.