Abstract
Many text mining applications, especially when investigating Text Classification (TC), require experiments to be performed using common text-collections, such that results can be compared with alternative approaches. With regard to single-label TC, most text-collections (textual data-sources) in their original form have at least one of the following limitations: the overall volume of textual data is too large for ease of experimentation; there are many predefined classes; most of the classes consist of only a very few documents; some documents are labeled with a single class whereas others have multiple classes; and there are documents found with little or no actual text-content. In this paper, we propose a standard approach to automatically extract “qualified” document-bases from a given textual data-source that can be used more effectively and reliably in single-label TC experiments. The experimental results demonstrate that document-bases extracted based on our approach can be used effectively in single-label TC experiments.
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Wang, Y.J., Sanderson, R., Coenen, F., Leng, P. (2008). Document-Base Extraction for Single-Label Text Classification. In: Song, IY., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2008. Lecture Notes in Computer Science, vol 5182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85836-2_34
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DOI: https://doi.org/10.1007/978-3-540-85836-2_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85835-5
Online ISBN: 978-3-540-85836-2
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