Feature-based forensic text comparison using a Poisson model for likelihood ratio estimation

Michael Carne, Shunichi Ishihara

    Research output: Contribution to conferencePaper

    Abstract

    Score- and feature-based methods are the two main ones for estimating a forensic likelihood ratio (LR) quantifying the strength of evidence. In this forensic text comparison (FTC) study, a score-based method using the Cosine distance is compared with a feature-based method built on a Poisson model with texts collected from 2,157 authors. Distance measures (e.g. Burrows’s Delta, Cosine distance) are a standard tool in authorship attribution studies. Thus, the implementation of a score-based method using a distance measure is naturally the first step for estimating LRs for textual evidence. However, textual data often violates the statistical assumptions underlying distance-based models. Furthermore, such models only assess the similarity, not the typicality, of the objects (i.e. documents) under comparison. A Poisson model is theoretically more appropriate than distance-based measures for authorship attribution, but it has never been tested with linguistic text evidence within the LR framework. The log-LR cost (Cllr) was used to assess the performance of the two methods. This study demonstrates that: (1) the feature-based method outperforms the score-based method by a Cllr value of ca. 0.09 under the best-performing settings and; (2) the performance of the feature-based method can be further improved by feature selection.
    Original languageEnglish
    Pages32-42
    Publication statusPublished - 2020
    Event18th Annual Workshop of the Australasian Language Technology Association - Australia
    Duration: 1 Jan 2020 → …

    Conference

    Conference18th Annual Workshop of the Australasian Language Technology Association
    Period1/01/20 → …

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