Augmenting Wikipedia-Extraction with Results from the Web

Not only is Wikipedia a comprehensive source of quality information, it has several kinds of internal structure (e.g., relational summaries known as infoboxes), which enable selfsupervised information extraction. While previous efforts at extraction from Wikipedia achieve high precision and recall on well-populated classes of articles, they fail in a larger number of cases, largely because incomplete articles and infrequent use of infoboxes lead to insufficient training data. This paper explains and evaluates a method for improving recall by extracting from the broader Web. There are two key advances necessary to make Web supplementation effective: 1) a method to filter promising sentences from Web pages, and 2) a novel retraining technique to broaden extractor recall. Experiments show that, used in concert with shrinkage, our techniques increase recall by a factor of up to 8 while maintaining or increasing precision.