2026 EACL EACL 2026

Style as Signature: Profile-Based Authorship Verification of Mihai Eminescu’s Journalistic Corpus

Abstract

AbstractAuthorship verification aims to assess whether a questioned text is stylistically compatible with an author’s known writings, a task that is particularly challenging in historical corpora with partial ground truth. We address this problem in the context of Mihai Eminescu’s journalistic corpus, a historically grounded collection comprising published articles, manuscripts, and texts of uncertain authorship. Using a profile-based framework with character n-grams and function words, we examine how stylistic compatibility behaves across different profile construction settings and temporal splits. The results show that character trigram profiles consistently accept verified texts while producing a small and stable set of rejections among disputed items, whereas function word profiles show near complete acceptance across the corpus. A qualitative analysis shows that rejected texts exhibit meaningful differences in discourse structure and communicative purpose. These findings illustrate how authorship verification can support literary scholarship through stable signals for close reading.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — profile-based method
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Natural Language Processing, Reinforcement Learning, Security & Privacy