WebSets: Extracting Sets of Entities from the Web Using Unsupervised Information Extraction
This addresses the need for scalable entity set extraction from web data, but it is incremental as it builds on existing clustering and pattern-based techniques.
The paper tackles the problem of extracting concept-instance pairs from HTML tables using an unsupervised information extraction method, achieving accurate results on multiple datasets.
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.