2023/01/29 by Avram, Sanda-Maria · 2 citations
Computer Science · #68T05 #68T07 (Secondary) #68T50 (Primary) 68T01 #Artificial Intelligence (cs.AI) #Authorship Attribution and Profiling #FOS: Computer and information sciences #I.2.0 #I.2.7 #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2301.12500
openalex publication_date 2023/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Being around for decades, the problem of Authorship Attribution is still very much in focus currently. Some of the more recent instruments used are the pre-trained language models, the most prevalent being BERT. Here we used such a model to detect the authorship of texts written in the Romanian language. The dataset used is highly unbalanced, i.e., significant differences in the number of texts per author, the sources from which the texts were collected, the time period in which the authors lived and wrote these texts, the medium intended to be read (i.e., paper or online), and the type of writing (i.e., stories, short stories, fairy tales, novels, literary articles, and sketches). The results are better than expected, sometimes exceeding 87% macro-accuracy.