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 conceptinstance 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.
Dalvi et al. REF described an open-domain information extraction method for extracting concept-instance pairs from an HTML corpus.
WebSets: extracting sets of entities from the web using unsupervised information extraction
WSDM '12
Computer Science Mathematics