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 IE. We show that this approach is competitive with current state-of-the-art IE algorithms based on specialized learning algorithms. We also introduce a new technique for improving the recall of our IE algorithm. This approach uses a two-level ensemble of classifiers to improve the recall of the extracted fragments while maintaining high precision. We show that this approach outperforms current state-of-the-art IE algorithms on several benchmark IE tasks. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Finn, A., & Kushmerick, N. (2004). Multi-level boundary classification for information extraction. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3201, pp. 111–122). Springer Verlag. https://doi.org/10.1007/978-3-540-30115-8_13
Mendeley helps you to discover research relevant for your work.