## 2 Related Works
However, every year more and more studies appear aimed at analyzing other languages. The purpose of the work [24] was the verification of the author of opinion pieces in Estonian and extraction unique to each author's stylistic features. The dataset contained 1474 opinion pieces (editorials, columns, etc.) written by 318 authors. The informative features were: word case, case of the first letter of the word, presence of digits in the word, POS tag frequencies, and verb type. In the first step, all texts encoded the matches of the patterns as bit vectors, where a true bit indicates a match. On the next calculation, the Matthews correlation between all resulting binary vectors was provided. Since the problem was set as a one-class learning problem, a One-Class SVM was chosen as the classifier. As a result, the proposed method had a precision of 74%. [PERSON] and [PERSON] devoted their study [25] to determining the authorship of Polish texts using stylometric features. The authors considered various feature generation methods (grammatical classes frequency, methods based on statistical features, and based on common word appearance). The generated feature sets included the most common and rarest words of the dataset, POS tag frequencies, as well as statistics of character numbers, words, and sentences for each text. The authors noted that Polish is a highly inflected language. Due to that fact, lemmas of words were used. Nine classification methods, including ridge regression, multilayer perceptron, KNN, RF, passive-aggressive, elastic net learned by stochastic gradient descent, Rocchio classifier, NB for multinomial and multivariate Bernoulli models were used. Proposed methods were tested for 105 novels and 1058 short fragments written by five Polish authors. Best accuracies were 79% and 100% for volumetric and short texts, respectively. The most accurate classifiers were stochastic gradient descent in the case of volumetric texts, and ridge regression and NB for multinomial models in the case of text fragments. The authors of [26] solved the problem aimed to determine the gender and age of the authors of Lithuanian texts. LSTM and CNN applied on the top of Lithuanian neural word embeddings were used. The dataset included Lithuanian parliamentary text transcripts, representing speeches and debates by the Lithuanian Seimas members. Texts with a length from 100 to 300 words were considered. For the age determination problem, the subset of 25,439 texts by 6 authors was used. For gender determination, 10,000 texts by two authors were used. Experiments were held with 6 balanced datasets of 100, 300, 500, 1000, 2000, and 5000 texts (i.e., instances) in each class. It was noted that LSTM worked more efficiently with small datasets, while CNN, in contrast, with large. The best accuracies of 32% and 61% for age and gender, respectively, were achieved using CNN with the largest datasets of 5000 instances in each class. An open-set attribution of Lithuanian Internet Comments was discussed in [27]. The authors developed the recommendation system that returns a list of alleged authors and their corresponding probabilities for further analysis by experts. A simple Winner-Takes-All (WTA) metric was used to evaluate models. The dataset included 200 authors and 200 texts. Frequency distributions of functional words, unigrams, bigrams, prefixes, abbreviations were chosen as features. One-class SVM was chosen as the classifier. For each author, 80% of the text was used as a training set and 20% as a test set. The One-class SVM results were used to generate a list of possible authors for each disputed text. The resulting list was ranked by the probability of the true author's position in the rank of the suspected authors. The process was repeated for each of the 200 authors for evaluation. For a list of 40 possible authors, the list precision accuracy was 80%, and for 80 authors was 90%.