Abstract
In this paper we focus on overcoming a common belief that accurate subject classification of text documents must involve high dimensional feature vectors. We study the fastText algorithm in terms of its ability to find and extract well distinguishable characteristics for a text corpora. In research we compare the achieved accuracy in the task of subject classification with various size of feature space selected. Finally, we attempt to discover the foundation behind fastText’s well performance.
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This work was sponsored by National Science Centre, Poland (grant 2016/21/B/ST6/02159).
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Walkowiak, T., Datko, S., Maciejewski, H. (2020). Low-Dimensional Classification of Text Documents. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Engineering in Dependability of Computer Systems and Networks. DepCoS-RELCOMEX 2019. Advances in Intelligent Systems and Computing, vol 987. Springer, Cham. https://doi.org/10.1007/978-3-030-19501-4_53
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DOI: https://doi.org/10.1007/978-3-030-19501-4_53
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