Abstract
The aim of the paper is to compare stylometric methods in a task of authorship, author gender and literacy period recognition for texts in Polish language. Different feature selection and classification methods were analyzed. Features sets include common words (the most common, the rarest and all words) and grammatical classes frequencies, as well as simple statistics of selected characters, words and sentences. Due to the fact that Polish is a highly inflected language common words features are calculated as the frequencies of the lexemes obtained by morpho-syntactic tagger for Polish. Nine different classifiers were analysed. Authors tested proposed methods on a set of Polish novels. Recognition was done on whole novels and chunked texts. Performed experiments showed that the best results are obtained for features based on all words. For ill defined problems (with small recognition accuracy) the random forest classifier gave the best results. In other cases (for tasks with medium or high recognition accuracy) the multilayer perceptron and the linear regression learned by stochastic gradient descent gave the best results. Moreover, the paper includes an analysis of statistical importance of used features.
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Baj, M., Walkowiak, T. (2017). Computer Based Stylometric Analysis of Texts in Polish Language. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_1
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