Abstract
In this work we investigate the use of various character, lexical, and syntactic level features and their combinations in automatic authorship attribution. Since the majority of text representation features are language specific, we examine their application on texts written in Croatian language. Our work differs from the similar work in at least three aspects. Firstly, we use slightly different set of features than previously proposed. Secondly, we use four different data sets and compare the same features across those data sets to draw stronger conclusions. The data sets that we use consist of articles, blogs, books, and forum posts written in Croatian language. Finally, we employ a classification method based on a strong classifier. We use the Support Vector Machines to learn classifiers which achieve excellent results for longer texts: 91% accuracy and F 1 measure for blogs, 93% acc. and F 1 for articles, and 99% acc. and F 1 for books. Experiments conducted on forum posts show that more complex features need to be employed for shorter texts.
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Reicher, T., Krišto, I., Belša, I., Šilić, A. (2010). Automatic Authorship Attribution for Texts in Croatian Language Using Combinations of Features. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15390-7_3
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DOI: https://doi.org/10.1007/978-3-642-15390-7_3
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