Expanding the Recall of Relation Extraction by Bootstrapping
Junji Tomita, Stephen Soderland, Oren Etzioni · 2006
Most works on relation extraction assume considerable human effort for making an annotated corpus or for knowledge engineering. Generic patterns employed in KnowItAll achieve unsupervised, high-precision extraction, but often result in low recall. This paper compares two bootstrapping methods to expand recall that start with automatically extracted seeds by KnowItAll. The first method is string pattern learning, which learns string contexts adjacent to a seed tuple. The second method learns less restrictive patterns that include bags of words and relation-specific named entity tags. Both methods improve the recall of the generic pattern method. In particular, the less restrictive pattern learning method can achieve a 250% increase in recall at 0.87 precision, compared to the generic pattern method.