Abstract
In this paper we are presenting a topic classification task for the morphologically complex Lithuanian and Russian languages, using popular supervised machine learning techniques. In our research we experimentally investigated two text classification methods and a big variety of feature types covering different levels of abstraction: character, lexical, and morpho-syntactic. In order to have comparable results for the both languages, we kept experimental conditions as similar as possible: the datasets were composed of the normative texts, taken from the news portals; contained similar topics; and had the same number of texts in each topic.
The best results (~0.86 of the accuracy) were achieved with the Support Vector Machine method and the token lemmas as a feature representation type. The character feature type capturing relevant patterns of the complex inflectional morphology without any external morphological tools was the second best. Since these findings hold for the both Lithuanian and Russian languages, we assume, they should hold for the entire group of the Baltic and Slavic languages.
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References
Ageev, M.S., Dobrov, B.V., Lukashevich, N.V., Sidorov, A.V.: Experimental search/classification algorithms and comparison with the “basic line”. In: All-Russian Scientific Conference (RCDL 2004), pp. 62–89 (2004). (in Russian)
Bina, B., Ahmadi, M.H., Rahgozar, M.: Farsi text classification using N-Grams and Knn algorithm a comparative study. In: Proceedings of the International Conference on Data Mining (DMIN 2008), pp. 385–390 (2008)
Boulis, C., Ostendorf, M.: Text classification by augmenting the bag-of-words representation with redundancy-compensated bigrams. In: Proceedings of the SIAM International Conference on Data Mining at the Workshop on Feature Selection in Data Mining, (SIAM-FSDM 2005) (2005)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Daudaravičius, V., Rimkutė, E., Utka, A.: Morphological annotation of the Lithuanian corpus. In: Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies (ACL 2007), pp. 94–99 (2007)
Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: Proceedings of the 7th International Conference on Information and Knowledge Management, pp. 148–155 (1998)
Forman, G., Guyon, I., Elisseeff, A.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)
Fortuna, B., Mladenič, D.: Using string kernels for classification of slovenian web documents. In: Proceedings of From Data and Information Analysis to Knowledge Engineering, pp. 358–365 (2005)
Gabrilovich, E., Markovitch, S.: Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5. In: Proceedings of the 21st International Conference on Machine Learning, pp. 321–328 (2004)
Gaustad, T., Bouma, G.: Accurate stemming of dutch for text classification. In: Proceedings of the Computational Linguistics in the Netherlands, pp. 104–117 (2002)
Gharib, T.F., Habib, M.B., Fayed, Z.T.: Arabic text classification using support vector machines. Int. J. Comput. Appl. 16(4), 192–199 (2009)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Hayes, P.J., Weinstein, S.P.: CONSTRUE/TIS: a system for content-based indexing of a database of news stories. In: Proceedings of the 2nd Conference on Innovative Applications of Artificial Intelligence (IAAI-90), pp. 49–64 (1990)
Hrala, M., Král, P.: Evaluation of the document classification approaches. In: Proceedings of the 8th International Conference on Computer Recognition Systems, pp. 877–885 (2013)
Hrala, M., Král, P.: Multi-label document classification in Czech. In: Proceedings of 16th International Conference on Text, Speech, and Dialogue, pp. 343–351 (2013)
Ikonomakis, M., Kotsiantis, S., Tampakas, V.: Text classification using machine learning techniques. WSEAS Trans. Comput. 8(4), 966–974 (2005)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Proceedings of ECML-98, 10th European Conference on Machine Learning 1398, pp. 137–142 (1998)
Kapočiūtė-Dzikienė, J., Vaassen, F., Daelemans, W., Krupavičius, A.: Improving topic classification for highly inflective languages. In: Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), pp. 1393–1410 (2012)
Khreisat, K.: Arabic text classification using N-gram frequency statistics: a comparative study. In: Proceedings of International Conference on Data Mining (DMIN 2006), pp. 78–82 (2006)
Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatica 31, 249–268 (2007)
Lehečka, J., Švec, J.: Improving multi-label document classification of Czech news articles. In: Proceedings of the 18th International Conference on Text, Speech and Dialogue, pp. 307–315 (2015)
Leopold, E., Kindermann, J.: Text categorization with support vector machines. how to represent texts in input space? Mach. Learn. 46(1–3), 423–444 (2002)
Lewis, D.D, Gale, W.A.: A sequential algorithm for training text classifiers. In: Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval (SIGIR-94), pp. 3–12 (1994)
Mackutė-Varoneckienė, A., Krilavičius, T., Morkevičius, V., Medelis, Ž.: Automatic Classification of Lithuanian Parliament Bills. Technical report No. 2014-CS-01, Baltic Institute of Advanced Technology, Vilnius, Lithuania, p. 6 (2014)
McCallum, A., Nigam, K.: A comparison of event models for Naive Bayes text classification. In: Proceedings of AAAI-98 Workshop on Learning for Text Categorization, pp. 41–48 (1998)
McNemar, Q.M.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157 (1947)
Nastase, V., Sayyad, J., Caropreso, M.F.: Using Dependency Relations for Text Classification. Technical report TR-2007-12, University of Ottawa, Ottawa, Canada, p. 13 (2007)
Peng, F., Schuurmans, D., Wang, S.: Language and task independent text categorization with simple language models. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology (NAACL 2003), vol. 1, pp. 110–117 (2003)
Radovanović, Miloš, Ivanović, Mirjana: Document representations for classification of short web-page descriptions. In: Tjoa, A.Min, Trujillo, Juan (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 544–553. Springer, Heidelberg (2006)
Rimkutė, E., Daudaravičius, V.: Morphological annotation of the Lithuanian corpus. Kalbų studijos 11, 30–35 (2007). (in Lithuanian)
Rogati, M., Yang, Y.: High-performing feature selection for text classification. In: Proceedings of the 11th International Conference on Information and Knowledge Management (CIKM 2002), pp. 659–661 (2002)
Saveski, M., Trajkovski I., Pehcevski J.: Classification of macedonian news articles. In: Proceedings of the Conference on Information Technologies for Young Researchers, pp. 1–5 (2011)
Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of International Conference on New Methods in Language Processing (1994)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)
Šilić, A., Chauchat, J.H., Bašić, B.D., Morin, A.: N-Grams and morphological normalization in text classification: a comparison on a Croatian-English parallel corpus. In: Proceedings of the 13th Portuguese Conference on Artificial Intelligence, pp. 671–682 (2007)
Sokyrko, A.B., Toldova, C.J.: Comparison of the effectiveness of two methods by removing the lexical and morphological ambiguity in the Russian language (hidden Markov model and syntactic parser) (2005). Technical report at http://www.aot.ru/docs/RusCorporaHMM.htm, (in Russian)
Stas, J., Zlacky, D., Hladek, D., Juhar, J.: Categorization of unorganized text corpora for better domain-specific language modeling. Adv. Electr. Electron. Eng. 11(5), 398–403 (2013)
Tan, C.M., Yuan-Fang, W., Chan-Do, L.: The use of bigrams to enhance text categorization. Inf. Process. Manage. 38(4), 529–546 (2002)
Tóth, J., Kondelová, A., Rozinaj, G.: Advanced text categorization methods with statistical approach. Electrorevue 4(2), 40–44 (2013)
Wahbeh, A., Al-Kabi, M., Al-Radaideh, Q.A., Al-Shawakfa, E.M., Alsmadi, I.: The effect of stemming on arabic text classification: an empirical study. Int. J. Inf. Retrieval Res. 1(3), 54–70 (2011)
Westa, Mateusz, Szymański, Julian, Krawczyk, Henryk: Text classifiers for automatic articles categorization. In: Rutkowski, Leszek, Korytkowski, Marcin, Scherer, Rafał, Tadeusiewicz, Ryszard, Zadeh, Lotfi A., Zurada, Jacek M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 196–204. Springer, Heidelberg (2012)
Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49 (1999)
Zhikov, V., Nikolova, I., Tolosi, L., Ivanov, Y., Georgiev, G.: Theme extraction in bulgarian: experiments in supervised and unsupervised settings. In: Proceedings of CLoBL 2012: Workshop on Computational Linguistics and Natural Language Processing of Balkan Languages (2012)
Zhikov, V., Nikolova, I., Tolosi, L., Ivanov, Y., Popov, B., Georgiev, G.: Enhancing social news media in bulgarian with natural language processing. INFOtheca 2(13), 6–18 (2012)
Zinkevičius, V.: Morphological Analysis with Lemuoklis. Darbai ir dienos 24, 246–273 (2000). (in Lithuanian)
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This research is funded by ESFA (DADA, VP1-3.1-ŠMM-10-V-02-025).
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Kapočiūtė-Dzikienė, J., Krilavičius, T. (2016). Topic Classification Problem Solving for Morphologically Complex Languages. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2016. Communications in Computer and Information Science, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-46254-7_41
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DOI: https://doi.org/10.1007/978-3-319-46254-7_41
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