We investigate the application of classification techniques to the problem of information extraction (IE). In particular we use support vector machines and several different feature-sets to build a set of classifiers for information extraction. We show that this approach is competitive with current state-ofthe-art information extraction algorithms based on specialized learning algorithms. We also introduce a new technique for improving the recall of IE systems called convergent boundary classification. We show that this can give significant improvement in the performance of our IE system and gives a system with both high precision and high recall.