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.
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References
Ng, V., Dasgupta, S., Arifin, S.: Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, Association for Computational Linguistics, pp. 611–618 (2006)
Durant, K.T., Smith, M.D.: Predicting the political sentiment of web log posts using supervised machine learning techniques coupled with feature selection. In: Nasraoui, O., Spiliopoulou, M., Srivastava, J., Mobasher, B., Masand, B. (eds.) WebKDD 2006. LNCS (LNAI), vol. 4811, pp. 187–206. Springer, Heidelberg (2007)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398. Springer, Heidelberg (1998)
Hotho, A., Maedche, A., Staab, S.: Ontology-based text document clustering. KI 16(4), 48–54 (2002)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
Liu, Z., Lv, X., Liu, K., Shi, S.: Study on SVM compared with the other text classification methods. In: 2010 Second International Workshop on Education Technology and Computer Science (ETCS), vol. 1, pp. 219–222. IEEE (2010)
Polpinij, J., Ghose, A.K.: An ontology-based sentiment classification methodology for online consumer reviews. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 01, pp. 518–524. IEEE Computer Society (2008)
Zhao, L., Li, C.: Ontology based opinion mining for movie reviews. In: Karagiannis, D., Jin, Z. (eds.) KSEM 2009. LNCS, vol. 5914, pp. 204–214. Springer, Heidelberg (2009)
Muller, H.M., Kenny, E.E., Sternberg, W.: Textpresso: an ontology-based information retrieval and extraction system for biological literature. PLoS Biol. 2(11), e309 (2004)
Lenat, D.B.: CYC: a large-scale investment in knowledge infrastructure. Commun. ACM 38(11), 33–38 (1995)
Pohl, A.: Classifying the wikipedia articles into the OpenCyc taxonomy. In: Rizzo, G., Mendes, P., Charton, E., Hellmann, S., Kalyanpur, A., (eds.) Proceedings of the Web of Linked Entities Workshop in Conjuction with the 11th International Semantic Web Conference, pp. 5–16 (2012)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support vector clustering. J. Mach. Learn. Res. 2, 125–137 (2002)
Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Piasecki, M., Szpakowicz, S., Broda, B.: A WordNet from the ground up. Oficyna Wydawnicza Politechniki Wrocawskiej, Wrocaw (2009)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)
Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web J. 5, 1–29 (2014)
Motik, B.: On the properties of metamodeling in owl. J. Logic Comput. 17(4), 617–637 (2007)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)
Mendes, P., Jakob, M., Bizer, C.: DBpedia for NLP: A Multilingual Cross-domain Knowledge Base. In: LREC (to appear, 2012)
Zhang, X., LeCun, Y.: Text understanding from scratch (2015). arXiv preprint arXiv:1502.01710
Agarwal, A., Chapelle, O., Dudík, M., Langford, J.: A reliable effective terascale linear learning system. J. Mach. Learn. Res. 15(1), 1111–1133 (2014)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
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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. https://doi.org/10.1007/978-3-319-39378-0_57
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