AcrosticSleuth: Probabilistic Identification and Ranking of Acrostics in Multilingual Corpora
This provides a statistical tool for scholars to efficiently detect acrostics across large multilingual corpora, addressing a manual and qualitative bottleneck in literary analysis.
The paper tackles the problem of automatically identifying acrostics in multilingual texts by developing AcrosticSleuth, a tool that formalizes it as a binary classification with extreme class imbalance, achieving F1 scores of 0.39, 0.59, and 0.66 on French, English, and Russian datasets, and discovering previously unknown instances like ARSPOETICA and Hobbes' signature.
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.