Abstract
We introduce a new stacking-like approach for multi-value classification. We apply this classification scheme using Naive Bayes, Rocchio and kNN classifiers on the well-known Reuters dataset. We use part-of-speech tagging for stopword removal. We show that our setup performs almost as well as other approaches that use the full article text even though we only classify headlines. Finally, we apply a Rocchio classifier on a dataset from a Web 2.0 site and show that it is suitable for semi-automated labelling (often called tagging) of short texts and is faster than other approaches.
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Heß, A., Dopichaj, P., Maaß, C. (2008). Multi-value Classification of Very Short Texts. In: Dengel, A.R., Berns, K., Breuel, T.M., Bomarius, F., Roth-Berghofer, T.R. (eds) KI 2008: Advances in Artificial Intelligence. KI 2008. Lecture Notes in Computer Science(), vol 5243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85845-4_9
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DOI: https://doi.org/10.1007/978-3-540-85845-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85844-7
Online ISBN: 978-3-540-85845-4
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