Linganisha mbinu

	Uchambuzi wa Uandishi (Stylometry)	Word2Vec
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Machine learning	Process / pipeline
Mwaka wa asili	2009	2013
Mwanzilishi	Mosteller & Wallace; Stamatatos	Tomas Mikolov et al.
Aina	Supervised stylometric classification	Neural word-embedding model
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.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Majina mbadala	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
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.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.