مقایسهٔ روش‌ها

	نسبت درستنمایی در جرم‌شناسی	Word2Vec
حوزه	علوم پزشکی قانونی	متن‌کاوی
خانواده	Regression model	Process / pipeline
سال پیدایش	2004	2013
پدیدآور	Colin Aitken & Franco Taroni	Tomas Mikolov et al.
نوع	Bayesian evidence evaluation model	Neural word-embedding model
منبع بنیادین	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.
نام‌های دیگر	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
مرتبط	3	4
خلاصه	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.