Using structured text for large-scale attribute extraction

We propose a weakly-supervised approach for extracting class attributes from structured text available within Web documents. The overall precision of the extracted attributes is around 30% higher than with previous methods operating on Web documents. In addition to attribute extraction, this approach also automatically identifies values for a subset of the extracted class attributes. Categories and Subject Descriptors H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing; I.2.7 [Artificial Intelligence]: Natural Language Processing; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms, Experimentation Keywords Weakly-supervised information extraction, class attribute extraction, knowledge acquisition, structured text collections

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Sujith Ravi, Marius Pasca
• Keywords: Attribute Extraction, CIKM 2008, Class Attribute, Information Management, Structured Text