Abstract
In multi-label text categorization, determining the final set of classes that will label a given document is not trivial. It implies first to determine whether a class is suitable of being attached to the text and, secondly, the number of them that we have to consider. Different strategies for determining the size of the final set of assigned labels are studied here. We analyze several classification algorithms along with two main strategies for selection: by a fixed number of top ranked labels, or using per-class thresholds. Our experiments show the effects of each approach and the issues to consider when using them.
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Montejo-Ráez, A., Ureña-López, L.A. (2006). Selection Strategies for Multi-label Text Categorization. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds) Advances in Natural Language Processing. FinTAL 2006. Lecture Notes in Computer Science(), vol 4139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816508_58
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DOI: https://doi.org/10.1007/11816508_58
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