Confidence Estimation for Knowledge Base Population

Information extraction systems automatically extract structured information from machine-readable documents, such as newswire, web, and multimedia. Despite significant improvement, the performance is far from perfect. Hence, it is useful to accurately estimate confidence in the correctness of the extracted information. Using the Knowledge Base Population Slot Filling task as a case study, we propose a confidence estimation model based on the Maximum Entropy framework, obtaining an average precision of 83.5%, Pearson coefficient of 54.2%, and 2.3% absolute improvement in F-measure score through a weighted voting strategy.