Comparison of SVM and Ontology-Based Text Classification Methods
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- Wróbel K., Wielgosz M., Smywiński-Pohl A., Pietron M. (2016) Comparison of SVM and Ontology-Based Text Classification Methods. In: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L., Zurada J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science, vol 9692. Springer, Cham
This work addresses the challenging task of text categorization. The main goal is the comparison of two different approaches, i.e. Vector Space Model and ontology-based solutions. The authors compare and contrast them with respect to accuracy and processing flow, which affect the classification results. The ontology-based method outperforms its counter-part when it comes to category resolution, i.e. the number of categories which can be processed. On the other hand, the SVM-based module is much faster and performs well when trained on an appropriately-structured learning set. The authors performed a series of tests to compare the methods and, as expected, the ontology-based solution outperformed the SVM classifier. It reached a micro averaged F1-score of 0.90 with 2.8 million Wikipedia articles, whereas the SVM-based module did not exceed 0.86 with the same data set. The macro averaged F1-score of both solutions was inferior to the micro one and reached values of 0.75 and 0.57, for ontology and SVM-based solutions respectively.