Learning relation extraction grammars with minimal human intervention: Strategy, results, insights and plans

Abstract

The paper describes the operation and evolution of a linguistically oriented framework for the minimally supervised learning of relation extraction grammars from textual data. Cornerstones of the approach are the acquisition of extraction rules from parsing results, the utilization of closed-world semantic seeds and a filtering of rules and instances by confidence estimation. By a systematic walk through the major challenges for this approach the obtained results and insights are summarized. Open problems are addressed and strategies for solving these are outlined. © 2011 Springer-Verlag.