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This paper proposes two novel normalisation techniques (Square Root Correction and Hapax Correction) for deriving likelihood ratios from the LambdaG authorship verification method without needing a separate calibration model, reducing data requirements and complexity while achieving comparable or superior performance in forensic text comparison.
This paper applies the likelihood ratio framework for forensic authorship attribution to Japanese texts, fusing stylometric features with embedding-based systems to improve discrimination and calibration.