Confidence Estimation Methods for Partially Supervised Relation Extraction
Eugene Agichtein · 2006
Text documents convey valuable information about entities and relations between entities that can be exploited in structured form for data mining, retrieval, and integration. A promising direction is a family of partially-supervised relation extraction systems that require little manual training. However, the output of such systems tend to be noisy, and hence it is crucial to be able to estimate the quality of the extracted information. We present Expectation-Maximization algorithms for automatically evaluating the quality of the extraction patterns and derived relation tuples. We demonstrate the effectiveness of our method on a variety of relations.