Сравнение методов

	Атрибуция авторства (стилометрия)	Word2Vec
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Machine learning	Process / pipeline
Год появления	2009	2013
Автор метода	Mosteller & Wallace; Stamatatos	Tomas Mikolov et al.
Тип	Supervised stylometric classification	Neural word-embedding model
Основополагающий источник	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.
Другие названия	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Связанные	3	4
Сводка	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.