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.

• Type: Journal
• Year: 2008
• Where: IS
• Authors: Zhu Zhang
• Keywords: Artificial Intelligence, Information Extraction Framework, IS 2008, Relation Classification Problem, Supervised Svm Classifier