# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 5 Discussion
### Analysis of Authors' Writing Styles  
The top three most discriminative features per author for the top-performing models on the 10-author set, on both FT and PP, are outlined in Table 13. In this context, the polarity refers to whether the classification model is positively or negatively influenced by the respective feature (\(+\) denotes positive polarity, while \(-\) denotes negative correlations). The importance of each feature is reflected in its corresponding coefficient from a simple Logistic Regression. These top features highlight distinct linguistic traits and syntactic structures that play an important role in identifying authorship within the provided texts. Further exploration and interpretation can provide relevant insights into and an understanding of the authors' writing styles and textual patterns. By considering the top features and their polarity in the classifier's decision-making process, we can redefine the distinctions of author writing styles and explore specific traits in greater detail. For an overview of the authors writing styles based on the most discriminative features in their texts and lexical statistics, we perform a cross-correlation between the data in Tables 2 and 13. Based on this correlation, [PERSON] tends to use a high number of connector links and repetitions, along with a rich vocabulary of unique nouns, which are negatively correlated with the number of words and unique words. This suggests a concise writing style with frequent repetitions. These features, particularly the frequent repetitions and unique nouns, play an important role in distinguishing [PERSON]'s texts from those of other authors. Nevertheless, the syntactic complexity represented by connector conjunctions and variations in dependency cases may pose challenges for classification models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Corpus** & **No. Docs** & **No. Authors** & **Input** & **Model** & **Method** & **F1** & **Error** & **Rank** \\ \hline FT & 400 & 10 authors & IPsG & Standalone & [17] & 0.79 & 0.20 & II \\ FT & 250 & 10 authors & RBI (KW 50) & Standalone & our method & **0.81** & **0.18** & I \\ PP & 3021 & 10 authors & RBI (KW 300) & Standalone & our method & 0.74 & 0.26 & III \\ \hline FT & 250 & 10 authors & RBI (KW 50) & ensemble learning & our method & **0.82** & **0.14** & I \\ PP & 3021 & 10 authors & RBI (KW 300) & ensemble learning & our method & 0.78 & 0.20 & II \\ \hline PP & 6832 & 10 authors & BERT Embeddings & BERT-base-ro & [51] & 0.85 & 0.14 & \\ PP & 3021 & 10 authors & BERT Embeddings & RoBERT & our method & 0.94 & 0.05 & II \\ FT & 250 & 10 authors & BERT Embeddings & RoBERT & our method & 0.88 & 0.11 & \\ PP & 3021 & 10 authors & BERT Embeddings + RBI & Hybrid RoBERT & our method & **0.95** & **0.04** & I \\ FT & 250 & 10 authors & BERT Embeddings + RBI & Hybrid RoBERT & our method & 0.94 & 0.05 & III \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison with existing methods for 10 authors (bold marks the best result). [PERSON]'s texts are characterized by high usage of punctuation and commas, indicating complex sentence structures and suggesting a focus on rhythm. Third-person pronouns and repetitions indicate a narrative style characterized by vivid descriptions and storytelling. These features are essential for identifying [PERSON]'s authorship, providing distinct markers of their writing style. [PERSON] tends to use a variety of unique words per paragraph, which suggests a diverse vocabulary. Yet, repetitive connectors and disjunctions indicate a potential lack of coherence in her texts as suggested by the negative polarity associated with these features. [PERSON]'s writing style is characterized by a high usage of pronouns and nominal subjects per paragraph, which may indicate a narrative style focusing on characters and their actions.