# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 2 Related Work on Authorship Attribution
### Classic Machine Learning Models  
Diverse machine learning (ML) algorithms have been applied to analyze and interpret linguistic and stylistic features extracted from the written texts. Techniques such as Support Vector Machines (SVMs), Random Forests (RF), or neural networks (NNs) learn patterns \begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & **Metrics** \\ \hline \multirow{4}{*}{Stylometric/Surface Features} & Average sentence length \\  & Word usage patterns \\  & Punctuation distribution \\  & Vocabulary richness \\ \hline \multirow{4}{*}{Lexical Features} & Word frequencies distribution \\  & Vocabulary size \\  & Unique words usage \\  & N-gram frequency \\ \hline \multirow{4}{*}{Syntactic Features} & Part-of-speech distribution \\  & Syntactic tree structure \\ \cline{1-1}  & Grammatical patterns \\ \cline{1-1}  & Sentence structure complexity \\ \cline{1-1}  & Dependency relations \\ \hline \multirow{4}{*}{Structural Features} & Document organization \\  & Paragraph and sentence structure \\ \cline{1-1}  & Heading usage \\ \cline{1-1}  & Document length \\ \cline{1-1}  & Formatting style \\ \hline \multirow{4}{*}{Content-Specific Features} & Genne-specific keywords \\ \cline{1-1}  & Theme-related phrases \\ \cline{1-1}  & Domain-specific terminology \\ \cline{1-1}  & Named entity recognition \\ \hline \multirow{4}{*}{Semantic Features} & Word semantics from WordNet \\ \cline{1-1}  & Pronoun usage \& Co-reference patterns \\ \cline{1-1}  & Cohesion \\ \cline{1-1}  & Semantic similarity measures using topic modeling (e.g., LDA), \\ \cline{1-1}  & static embeddings (e.g., Word2 Vec and GloVe), or contextualized \\ \cline{1-1}  & embeddings (e.g., BERT) \\ \hline \multirow{2}{*}{Discourse Features} & Coherence measures \\ \cline{1-1}  & Connective words usage \\ \hline \multirow{4}{*}{Time-Based Features} & Temporal patterns of word usage \\ \cline{1-1}  & Writing style evolution over time \\ \cline{1-1}  & Date and time of document creation \\ \cline{1-1}  & Writing trends \\ \cline{1-1}  & Time-sensitive vocabulary \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of linguistic features integrated in subsequent experiments. and associations within the data, enabling models to discern the unique writing styles of different authors. The success of ML-based authorship attribution predominantly relies on the careful selection and engineering of features. Statistical methods play a central role in quantitatively distinguishing an author's writing style. Computational stylometry employs methodologies such as word length, sentence length, vocabulary richness, and frequency analysis to analyze texts. Each author possesses a distinct writing style, which can be quantitatively differentiated using statistical techniques. The process involves text pre-processing, feature extraction, classification, and author attribution, with various classifiers utilized for this purpose. [PERSON] et al. [18] leveraged a fuzzy learning classifier and an SVM, with the SVM model achieving higher accuracy in author identification. Combining these classifiers resulted in improved accuracy compared to individual SVM and fuzzy classifier approaches. Diverse ML classifiers have been employed across various authorship analysis tasks. [PERSON] et al. [19] introduced a stacked classifier for source code authorship attribution, aiming to identify the author of a given code in the context of open-source repositories like GitHub, Codalab, Kaggle, and Codeforces online judge. With the increasing number of software submissions, there is a growing concern about code plagiarism. The authors used a TF-IDF-based mechanism to represent the source code, utilizing word and character n-grams to generate code vectors. These vectors were then fed to various ML classifiers for prediction. Their approach achieved an accuracy of 82.95% on the test data. The practical application of their method extends to detecting plagiarized code and helping prevent legal issues in software development. [PERSON] et al.