手法を比較

	著者の帰属推定（文体測定学）	テキスト分類	Word2Vec
分野	テキストマイニング	テキストマイニング	テキストマイニング
系統	Machine learning	Process / pipeline	Process / pipeline
提唱年	2009	—	2013
提唱者	Mosteller & Wallace; Stamatatos	—	Tomas Mikolov et al.
種類	Supervised stylometric classification	Supervised NLP classification task	Neural word-embedding model
原典	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.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
別名	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	text categorization, document classification, topic classification, metin sınıflandırma	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
関連	3	4	4
概要	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.	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.