Self-supervised Relation Extraction from the Web

• Published in: Foundations of Intelligent Systems pp. 755 - 764
• Main Authors: Feldman, Ronen, Rosenfled, Benjamin, Soderland, Stephen, Etzioni, Oren
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; Information Extraction; Negative Sentence; Noun Phrase; Relation Instance; Software; True Recall; Performance evaluation; Entity relationship model; World wide web; Text; Internet; Artificial intelligence; Pattern extraction
• ISBN: 9783540457640; 354045764X
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11875604_84

Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. SRES is a self-supervised Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target elations and their attributes. SRES automatically generates the training data needed for its pattern-learning component. We also compare the performance of SRES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which uses a simpler and less powerful pattern language than SRES.