Abstract
This paper deals with one class automatic document classification. Five feature selection methods and three classifiers are evaluated on a Czech corpus in order to build an efficient Czech document classification system. Lemmatization and POS tagging are used for a precise representation of the Czech documents. We demonstrated, that POS tag filtering is very important, while the lemmatization plays a marginal role for classification.We also showed that Maximum Entropy and Support Vector Machines are very robust to the feature vector size and outperform significantly the Naive Bayes classifier from the view point of the classification accuracy. The best classification accuracy is about 90% which is enough for an application for the Czech News Agency, our commercial partner.
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References
Bratko, A., Bogdan, F.: Exploiting structural information for semi-structured document categorization. Information Processing and Management, 679–694 (2004)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Della Pietra, S., Della Pietra, V., Lafferty, J.: Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 380–393 (1997)
Forman, G., Guyon, I., Elisseeff, A.: An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research 3, 1289–1305 (2003)
Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning, Nashville, US, pp. 412–420. Morgan Kaufmann Publishers, San Francisco (1997)
Luo, X., Zincir-Heywood, A.N.: Incorporating Temporal Information for Document Classification. In: ICDE Workshops, pp. 780–789 (2007)
Galavotti, L., Sebastiani, F., Simi, M.: Experiments on the Use of Feature Selection and Negative Evidence in Automated Text Categorization. In: Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries, pp. 59–68. Springer, London (2000)
Cover, T., Thomas, J.: Elements of information theory. Wiley, Chichester (1991)
Lim, C.S., Lee, K.J., Kim, G.C.: Multiple sets of features for automatic genre classification of web documents. Information Processing and Management 41, 1263–1276 (2005)
Gomez, J.C., Moens, M.-F.: PCA document reconstruction for email classification. Computer Statistics and Data Analysis 56, 741–751 (2012)
Yun, J., Jing, L., Yu, J., Huang, H.: A multi-layer text classification framework based on two-level representation model. Expert Systems with Applications 39, 2035–2046 (2012)
Hajič, J., Böhmová, A., Hajičová, E., Vidová-Hladká, B.: The Prague Dependency Treebank: A Three-Level Annotation Scenario. In: Abeillé, A. (ed.) Treebanks: Building and Using Parsed Corpora, pp. 103–127. Kluwer, Amsterdam (2000)
Cohen, W.W.: MinorThird: Methods for Identifying Names and Ontological Relations in Text using Heuristics for Inducing Regularities from Data (2004), http://minorthird.sourceforge.net
Ponmuthuramalingam, P., Devi, T.: Effective Term Based Text Clustering Algorithms. International Journal on Computer Science and Engineering, 1665–1673 (2010)
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Hrala, M., Král, P. (2013). Evaluation of the Document Classification Approaches. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_86
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DOI: https://doi.org/10.1007/978-3-319-00969-8_86
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