@inproceedings{carne-ishihara-2020-feature,
    title = "Feature-Based Forensic Text Comparison Using a {P}oisson Model for Likelihood Ratio Estimation",
    author = "Carne, Michael  and
      Ishihara, Shunichi",
    editor = "Kim, Maria  and
      Beck, Daniel  and
      Mistica, Meladel",
    booktitle = "Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.alta-1.4/",
    pages = "32--42",
    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."
}