Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian

Authorship attribution for less-resourced languages like Romanian, characterized by the scarcity of large, annotated datasets and the limited number of available NLP tools, poses unique challenges. This study focuses on a hybrid Transformer combining handcrafted linguistic features, ranging from sur...

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Auteurs principaux:	Melania Nitu, Mihai Dascalu
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2024-03-01
Collection:	Applied Sciences
Sujets:	authorship attribution linguistic features contextualized embeddings hybrid Transformer ensemble learning linguistic analysis
Accès en ligne:	https://www.mdpi.com/2076-3417/14/7/2700
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