Abstract
In this work we evaluate two different methods for deriving features for a subject classification of text documents. The first method uses the standard Bag-of-Words (BoW) approach, which represents the documents with vectors of frequencies of selected terms appearing in the documents. This method heavily relies on the natural language processing (NLP) tools to properly preprocess text in the grammar- and inflection-conscious way. The second approach is based on the word-embedding technique recently proposed by Mikolov and does not require any NLP preprocessing. In this method the words are represented as vectors in continuous space and this representation of words is used to construct the feature vectors of the documents. We evaluate these fundamentally different approaches in the task of classification of Polish language Wikipedia articles with 34 subject areas. Our study suggests that the word-embedding based features seem to outperform the standard NLP-based features providing sufficiently large training dataset is available.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Eder, M., Piasecki, M., Walkowiak, T.: An open stylometric system based on multilevel text analysis. Cogn. Stud.—Etudes Cogn. (17) (2017). https://doi.org/10.11649/cs.1430
Goodman, J.: Classes for fast maximum entropy training. In: Proceedings of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, (Cat. No.01CH37221), vol. 1, pp. 561–564 (2001). https://doi.org/10.1109/ICASSP.2001.940893
Harris, Z.: Distributional structure. Word (1954)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Short Papers, vol. 2, pp. 427–431. Association for Computational Linguistics (2017). http://aclweb.org/anthology/E17-2068
Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013). http://arxiv.org/abs/1301.3781
Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751. Association for Computational Linguistics, Atlanta, June 2013. http://www.aclweb.org/anthology/N13-1090
Młynarczyk, K., Piasecki, M.: Wiki test - 34 categories (2015). http://hdl.handle.net/11321/217. CLARIN-PL digital repository
Młynarczyk, K., Piasecki, M.: Wiki train - 34 categories (2015). http://hdl.handle.net/11321/222. CLARIN-PL digital repository
Radziszewski, A.: A tiered CRF tagger for Polish. In: Bembenik, R., Skonieczny, L., Rybinski, H., Kryszkiewicz, M., Niezgodka, M. (eds.) Intelligent Tools for Building a Scientific Information Platform. Studies in Computational Intelligence, vol. 467, pp. 215–230. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35647-6_16
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1986)
Torkkola, K.: Discriminative features for text document classification. Formal Pattern Anal. Appl. 6(4), 301–308 (2004). https://doi.org/10.1007/s10044-003-0196-8
Walkowiak, T.: Language processing modelling notation - orchestration of NLP microservices. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds.) DepCoS-RELCOMEX 2017. AISC, pp. 464–473. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-59415-6_44
Walkowiak, T., Malak, P.: Polish texts topic classification evaluation. In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence, ICAART 2018, vol. 2, pp. 515–522. INSTICC, SciTePress (2018)
Acknowledgement
This work was sponsored by National Science Centre, Poland (grant 2016/21/B/ST6/02159).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Walkowiak, T., Datko, S., Maciejewski, H. (2018). Feature Extraction in Subject Classification of Text Documents in Polish. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-91262-2_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91261-5
Online ISBN: 978-3-319-91262-2
eBook Packages: Computer ScienceComputer Science (R0)