Comparer des méthodes

	Attribution d'auteur (Stylométrie)	Classification de texte
Domaine	Fouille de textes	Fouille de textes
Famille	Machine learning	Process / pipeline
Année d'origine	2009	—
Auteur d'origine	Mosteller & Wallace; Stamatatos	—
Type	Supervised stylometric classification	Supervised NLP classification task
Source fondatrice	Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3), 538–556.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Alias	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	text categorization, document classification, topic classification, metin sınıflandırma
Apparentées	3	4
Résumé	Authorship attribution is the task of identifying the most probable author of an anonymous or disputed text by analysing its stylistic fingerprint. Rooted in the statistical work of Mosteller and Wallace on the Federalist Papers (1964), the field was systematically surveyed and formalised by Stamatatos (2009), who catalogued feature sets ranging from character n-grams and function-word frequencies to syntactic and semantic representations used by modern machine-learning classifiers.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.