Information Extraction by Convergent Boundary Classification

Aidan Finn and Nicholas Kushmerick

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.


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