Abstract
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.
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Keywords
- Support Vector Machine
- Information Extraction
- Inductive Logic Programming
- Negative Instance
- Token Feature
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2004 Springer-Verlag Berlin Heidelberg
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Finn, A., Kushmerick, N. (2004). Multi-level Boundary Classification for Information Extraction. In: Boulicaut, JF., Esposito, F., Giannotti, F., Pedreschi, D. (eds) Machine Learning: ECML 2004. ECML 2004. Lecture Notes in Computer Science(), vol 3201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30115-8_13
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DOI: https://doi.org/10.1007/978-3-540-30115-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23105-9
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