Vertaile menetelmiä

	Tekijyyden attribuutio (stilometri)	Word2Vec
Tieteenala	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Machine learning	Process / pipeline
Syntyvuosi	2009	2013
Kehittäjä	Mosteller & Wallace; Stamatatos	Tomas Mikolov et al.
Tyyppi	Supervised stylometric classification	Neural word-embedding model
Alkuperäislähde	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.
Rinnakkaisnimet	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Liittyvät	3	4
Tiivistelmä	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.