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Mining relational data from text: From strictly supervised to weakly supervised learning

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Mining relational data from text: From strictly supervised to weakly supervised learning
This paper approaches the relation classification problem in information extraction framework with different machine learning strategies, from strictly supervised to weakly supervised. A number of learning algorithms are presented and empirically evaluated on a standard data set. We show that a supervised SVM classifier using various lexical and syntactic features can achieve competitive classification accuracy. Furthermore, a variety of weakly supervised learning algorithms can be applied to take advantage of large amount of unlabeled data when labeling is expensive. Newly introduced randomsubspace-based algorithms demonstrate their empirical advantage over competitors in the context of both active learning and bootstrapping. r 2007 Elsevier B.V. All rights reserved.
Zhu Zhang
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where IS
Authors Zhu Zhang
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