Abstract
Automatic text categorisation of documents has received a resounding interest in last years due to the increased availability of documents in digital form and the commanding need to organize them. In this paper, our main focus is the development of tools that will enable very fast and accurate text classifiers in large scale databases. To pursue this objective, we start by introducing the main issues of text categorisation and present possible ways of handling them. Kernel based methods, such as, Support Vector Machines (SVMs), are learning methods with strong potential for solving the tasks involved in automatic text categorisation. The first results achieved with Reuters-21578 collection are reported and some points of possible improvements are identified.
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© 2003 Springer-Verlag Wien
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Silva, C., Ribeiro, B. (2003). An Inductive Inference Approach to Large Scale Text Categorisation. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_24
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DOI: https://doi.org/10.1007/978-3-7091-0646-4_24
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-00743-3
Online ISBN: 978-3-7091-0646-4
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