Abstract
In this paper, we present our improvement of a multi-label document classifier for text filtering in a corpus containing Czech news articles, where relevant topics of an arbitrary document are to be assigned automatically. Different vector space models, different classifiers and different thresholding strategies were investigated and the performance was measured in terms of sample-wise average \(F_1\) score. Results of this paper show that we can improve the performance of our baseline naive Bayes classifier by 25% relatively when using linear SVC classifier with sublinear tf-idf vector space model, and another 6.1% relatively when using regressor-based sample-wise thresholding strategy.
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Lehečka, J., Švec, J. (2015). Improving Multi-label Document Classification of Czech News Articles. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_35
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DOI: https://doi.org/10.1007/978-3-319-24033-6_35
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