weakly-supervised relation classification for information extraction
This paper approaches the relation classification problem in information extraction framework with bootstrapping on top of Support Vector Machines. A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus. We show that the supervised SVM classifier using various lexical and syntactic features can achieve promising classification accuracy. More importantly, the proposed  BootProject  algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.
relation classification information extraction bootstrapping svm co-training