# Authorship Attribution on Short Texts in the Slovenian Language
## 2 Related Work  
Researchers have long been interested in the subject of authorship attribution. With the goal of algorithmically attributing the authorship of written text to a specific person, various methods can be used. Statistical methods are already well established, but machine-learning-based methods are now becoming increasingly important [2; 12]. It is possible to determine the authorship of a text algorithmically due to the difference between styles of people's writing--they can differ in the choice and frequency of stop word usage, usage of punctuation, and sentence length, among others [13]. Recent developments in the field of language models indicated that pre-trained models can be very suitable for various natural language processing tasks, such as text classification [14]. [PERSON] and [PERSON] [15] were among the first to use language models to tackle the problem of authorship attribution, reaching an accuracy of around 0.8 on a dataset of 21 authors. They used language models such as BERT, ELMO, and GPT-2, all of which performed rather similarly. [PERSON] et al. [11] proposed combining the BERT model with the use of additional stylometric features to solve the closed-class problem type on short texts, which yielded promising results. However, the authors did not evaluate the performance of their method for the open-class problem of authorship attribution. [PERSON] and [PERSON] [16] used deep learning (DL) methods to determine the authorship of a historical text. They used a convolutional neural network (CNN) to determine with 86% accuracy that a certain individual was the author of the text. It is worth noting that the authors used single sentences from longer works to train the CNN, which means that training data are similar to the short texts in our problem domain. Methods based on machine learning (ML) were also used to solve a similar problem called writer identification. [PERSON] et al. [17] compared DL methods against traditional ML methods for recognizing scribes from handwriting images and found that DL-based systems performed at least as well as the traditional ML methods. [PERSON] et al. [18] compared various machine learning methods to determine the authorship of Russian literary and short texts. They used classical machine learning approaches, BERT, support vector machine, genetic algorithms and more. However, despite the complexity of their methods, they reached an accuracy of 73% for shorter social media comments. Furthermore, despite belonging to the same language group, Russian and Slovenian are mutually unintelligible. Furthermore, as described in the introduction, Slovenian has a number of rare linguistic features, so the methods may not be directly transferable. In Slovenian, very few approaches have been published that address the problem of authorship attribution. [PERSON] [19] tested a variety of computational approaches based on stylometry (the most effective approach was the Naive Bayes classifier), reaching an accuracy of 75%. [PERSON] [20] attempted to determine whether some notable Slovenian authors truly authored all of their works, but did not attempt to generalize the method or numerically quantify the results. [PERSON] [21] used multivariate analysis to determine whether a literary work of unknown authorship was written by a specific Slovenian author who is rumored to have written that work. They analyzed the usage of the most common words in Slovenian with principal component analysis and discriminant analysis. They found that for the specific circumstances of their research, their methods were able to give a clear answer regarding the authorship. However, the above-mentioned approaches focused on analyzing \"long\" literary works, not shorter texts. The most notable recent progress in natural language processing (NLP) in Slovenian was the introduction of SloBERTa [8], a language model with a structure similar to RoBERTa. Up-to-date benchmarks are also being introduced, such as the Slovenian version of SuperGLUE [22].