Comparar métodos

	Atribuição de Autoria (Estilometria)	Forensic Likelihood Ratio	Classificação de Texto	Word2Vec
Área	Mineração de texto	Ciências forenses	Mineração de texto	Mineração de texto
Família	Machine learning	Regression model	Process / pipeline	Process / pipeline
Ano de origem	2009	2004	—	2013
Autor original	Mosteller & Wallace; Stamatatos	Colin Aitken & Franco Taroni	—	Tomas Mikolov et al.
Tipo	Supervised stylometric classification	Bayesian evidence evaluation model	Supervised NLP classification task	Neural word-embedding model
Fonte seminal	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	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.
Outros nomes	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	Bayes Factor in Forensics, Forensic Evidence Weight, LR-Based Forensic Evaluation, Adli Olabilirlik Oranı	text categorization, document classification, topic classification, metin sınıflandırma	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Relacionados	3	3	4	4
Resumo	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.	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.