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WebSets: Extracting Sets of Entities from the Web Using Unsupervised Information Extraction
lib:d464196e05b0e42b (v1.0.0)
Authors: Bhavana Dalvi,William W. Cohen,Jamie Callan
ArXiv: 1307.0261
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1307.0261v1
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.
ArXiv: 1307.0261
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1307.0261v1
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.
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