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We describe a open-domain information extraction method for extractingconcept-instance pairs from an HTML corpus. Most earlier approaches to thisproblem rely on combining clusters of distributionally similar terms andconcept-instance pairs obtained with Hearst patterns. In contrast, our methodrelies on a novel approach for clustering terms found in HTML tables, and thenassigning concept names to these clusters using Hearst patterns. The method canbe efficiently applied to a large corpus, and experimental results on severaldatasets show that our method can accurately extract large numbers ofconcept-instance pairs.

WebSets: Extracting Sets of Entities from the Web Using Unsupervised Information Extraction