AcrosticSleuth: Probabilistic Identification and Ranking of Acrostics in Multilingual Corpora
<|reference_start|>AcrosticSleuth: Probabilistic Identification and Ranking of Acrostics in Multilingual Corpora: For centuries, writers have hidden messages in their texts as acrostics, where initial letters of consecutive lines or paragraphs form meaningful words or phrases. Scholars searching for acrostics manually can only focus on a few authors at a time and often favor qualitative arguments in discussing intentionally. We aim to put the study of acrostics on firmer statistical footing by presenting AcrosticSleuth, a first-of-its-kind tool that automatically identifies acrostics and ranks them by the probability that the sequence of characters does not occur by chance (and therefore may have been inserted intentionally). Acrostics are rare, so we formalize the problem as a binary classification task in the presence of extreme class imbalance. To evaluate AcrosticSleuth, we present the Acrostic Identification Dataset (AcrostID), a collection of acrostics from the WikiSource online database. Despite the class imbalance, AcrosticSleuth achieves F1 scores of 0.39, 0.59, and 0.66 on French, English, and Russian subdomains of WikiSource, respectively. We further demonstrate that AcrosticSleuth can identify previously unknown high-profile instances of wordplay, such as the acrostic spelling ARSPOETICA (``art of poetry") by Italian Humanist Albertino Mussato and English philosopher Thomas Hobbes' signature in the opening paragraphs of The Elements of Law.<|reference_end|>
@article{fedchin2024acrosticsleuth:,
    title={AcrosticSleuth: Probabilistic Identification and Ranking of Acrostics in
  Multilingual Corpora},
    author={Aleksandr Fedchin, Isabel Cooperman, Pramit Chaudhuri, Joseph P.
  Dexter},
    journal={arXiv preprint arXiv:2408.04427},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04427},
    primaryClass={cs.CL}
}