Szczegóły publikacji
Opis bibliograficzny
Comparison of SVM and ontology-based text classification methods / Krzysztof WRÓBEL, Maciej WIELGOSZ, Aleksander SMYWIŃSKI-POHL, Marcin PIETROŃ // W: Artificial intelligence and soft computing : 15th international conference, ICAISC 2016 : Zakopane, Poland, June 12–16, 2016 : proceedings, Pt. 1 / eds. Leszek Rutkowski, [et al.]. — Switzerland : Springer International Publishing, cop. 2016. — (Lecture Notes in Artificial Intelligence ; ISSN 0302-9743 ; 9692). — ISBN: 978-3-319-39377-3; e-ISBN: 978-3-319-39378-0. — S. 667–680. — Bibliogr. s. 679–680, Abstr. — Toż na Dysku Flash. — K. Wróbel - afiliacja: Uniwersytet Jagiellońki, A. Smywiński-Pohl – dod. afiliacja: Jagiellonian University
Autorzy (4)
- Wróbel Krzysztof
- AGHWielgosz Maciej
- AGHSmywiński-Pohl Aleksander
- AGHPietroń Marcin
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 98359 |
|---|---|
| Data dodania do BaDAP | 2016-06-20 |
| DOI | 10.1007/978-3-319-39378-0_57 |
| Rok publikacji | 2016 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | 15th International Conference on Artificial Intelligence and Soft Computing |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
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. © Springer International Publishing Switzerland 2016.