Abstract
Automated text categorisation systems learn a generalised hypothesis from large numbers of labelled examples. However, in many domains labelled data is scarce and expensive to obtain. Active learning is a technique that has shown to reduce the amount of training data required to produce an accurate hypothesis. This paper proposes a novel method of incorporating predictions made in previous iterations of active learning into the selection of informative unlabelled examples. We show empirically how this method can lead to increased classification accuracy compared to alternative techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys (CSUR) 34(1), 1–47 (2002)
Mitchell, T.M.: Machine Learning. McGraw-Hill Higher Education, New York (1997)
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Proceedings of the 17th International ACM SIGIR, pp. 3–12. ACM Press, New York (1994)
McCallum, A., Nigam, K.: Employing EM in Pool-Based Active learning for Text Classification. In: Proceedings of the 15th International Conference on Machine Learning, pp. 350–358 (1998)
Schohn, G., Cohn, D.: Less is more: Active learning with Support Vector Machines. In: Proceedings of the 17th International Conference on Machine Learning, pp. 285–286 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Davy, M., Luz, S. (2007). Active Learning with History-Based Query Selection for Text Categorisation. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_71
Download citation
DOI: https://doi.org/10.1007/978-3-540-71496-5_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71494-1
Online ISBN: 978-3-540-71496-5
eBook Packages: Computer ScienceComputer Science (R0)