Σύγκριση μεθόδων

	Εκτίμηση Πιθανοφάνειας (Forensic Likelihood Ratio - LR)	Ταξινόμηση Κειμένου	Word2Vec
Πεδίο	Εγκληματολογικές Επιστήμες	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Regression model	Process / pipeline	Process / pipeline
Έτος προέλευσης	2004	—	2013
Δημιουργός	Colin Aitken & Franco Taroni	—	Tomas Mikolov et al.
Τύπος	Bayesian evidence evaluation model	Supervised NLP classification task	Neural word-embedding model
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	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
Συναφείς	3	4	4
Σύνοψη	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.