Linganisha mbinu

	Uchambuzi wa Uandishi (Stylometry)	Uainishaji wa Maandishi
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Machine learning	Process / pipeline
Mwaka wa asili	2009	—
Mwanzilishi	Mosteller & Wallace; Stamatatos	—
Aina	Supervised stylometric classification	Supervised NLP classification task
Chanzo asilia	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.
Majina mbadala	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	text categorization, document classification, topic classification, metin sınıflandırma
Zinazohusiana	3	4
Muhtasari	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.