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A Likelihood Ratio-Based Forensic Text Comparison in SMS Messages: A Fused System with Lexical Features and N-Grams
Abstract
This chapter is built on two studies: Ishihara (2011) “A Forensic Authorship Classification in SMS Messages: A Likelihood Ratio-Based Approach Using N-Grams” and Ishihara (2012) “A Forensic Text Comparison in SMS Messages: A Likelihood Ratio Approach with Lexical Features.” They are two of the first Likelihood Ratio (LR)-based forensic text comparison studies in forensic authorship analysis. The author attribution was modelled using N-grams in the former, whereas it was modelled using so-called lexical features in the latter. In the current study, the LRs obtained from these separate experiments are fused using a logistic regression fusion technique, and the author reports how much improvement in performance the fusion brings to the LR-based forensic text comparison system. The performance of the fused system is assessed based on the magnitude of the fused LRs using the log-likelihood-ratio cost (Cllr). The strength of the fused LRs is graphically presented in Tippett plots and compared with those of the original LRs. The chapter demonstrates that the fused system outperforms the original systems.
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