  We describe a open-domain information extraction method for extracting
concept-instance pairs from an HTML corpus. Most earlier approaches to this
problem rely on combining clusters of distributionally similar terms and
concept-instance pairs obtained with Hearst patterns. In contrast, our method
relies on a novel approach for clustering terms found in HTML tables, and then
assigning concept names to these clusters using Hearst patterns. The method can
be efficiently applied to a large corpus, and experimental results on several
datasets show that our method can accurately extract large numbers of
concept-instance pairs.
