手法を比較

	著者の帰属推定（文体測定学）	Forensic Likelihood Ratio	Word2Vec
分野	テキストマイニング	法科学	テキストマイニング
系統	Machine learning	Regression model	Process / pipeline
提唱年	2009	2004	2013
提唱者	Mosteller & Wallace; Stamatatos	Colin Aitken & Franco Taroni	Tomas Mikolov et al.
種類	Supervised stylometric classification	Bayesian evidence evaluation model	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.	Aitken, C. G. G., & Taroni, F. (2004). Statistics and the Evaluation of Evidence for Forensic Scientists (2nd ed.). Wiley. ISBN: 978-0-470-84367-3	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	Bayes Factor in Forensics, Forensic Evidence Weight, LR-Based Forensic Evaluation, Adli Olabilirlik Oranı	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
関連	3	3	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.	The Forensic Likelihood Ratio (LR) is a Bayesian framework for quantifying the weight of forensic evidence relative to two competing propositions — typically the prosecution and defence hypotheses. Formally developed and systematised by Colin Aitken and Franco Taroni in their 2004 Wiley monograph, the LR expresses how much more probable the observed evidence is under one hypothesis than under the other, providing the court with a single, interpretable number that separates the scientist's role from the fact-finder's role.	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.