Leveraging Part-of-Speech Tagging for Enhanced Stylometry of L atin Literature (2024)

Abstract: In literary critical applications, stylometry can benefit from hand-curated feature sets capturing various syntactic and rhetorical functions. For premodern languages, calculation of such features is hampered by a lack of adequate computational resources for accurate part-of-speech tagging and semantic disambiguation. This paper reports an evaluation of POS-taggers for Latin and their use in augmenting a hand-curated stylometric feature set. Our experiments show that POS-augmented features not only provide more accurate counts than POS-blind features but also perform better on tasks such as genre classification. In the course of this work we introduce POS n-grams as a feature for Latin stylometry.

## introduction although most associated with studies of authorship attribution and chronology @xcite @xcite , computational stylometric methods have increasingly been deployed to address broader literary questions and to augment more traditional approaches to criticism @xcite @xcite @xcite . for premodern literary traditions, such work has encompassed applications ranging from profiling the evolution of latin prose style to computational restoration of greek inscriptions and manuscripts @xcite @xcite , as well as genre classification across multiple languages @xcite @xcite . for instance, for their genre classification work @xcite developed a set of 26 latin stylometric features, including curated function word lists (e.g., prepositions, conjunctions, and pronouns), subordinate clauses, and sentence and clause length. although calculated using only word or character n-gram counts and a small number of language-specific heuristics, these features proved highly effective for genre classification of classical texts, with the best-performing models achieving @xmath0. it is the strength of a model, however, that it can withstand the frailties of individual features, at least up to a point. hand-curated lists of words, such as those employed by @xcite , may be insensitive to homonyms, semantic ambiguity, and other potentially challenging facets of natural language. while such issues may not impede success on certain tasks, increasing the accuracy of feature counts may be essential for others, especially those involving fine distinctions. recent developments in nlp for latin have led to the creation of tools that can plausibly improve on existing stylometric methods. notably, the evalatin 2020 campaign @xcite proposed shared tasks in lemmatization and part-of-speech (pos) tagging for classical latin. submissions introduced pos tagger models based on gradient boosters (celano, 2020), ensemble methods @xcite , and lstms @xcite that achieved accuracies of up to 96%. here, we evaluate several pos taggers and assess how they improve and expand the feature set published by @xcite . we perform error analysis on our pos-augmented features to quantify these improvements. we also train a classifier to distinguish latin verse from prose using either a pos-augmented or the original feature set, and we compare the accuracy and feature importances for each set. in doing so, we demonstrate the stylometric and literary relevance of pos-augmented features and showcase a transition from general tool development to specific literary applications in a lower-resource language. ## conclusion we evaluate state-of-the-art pos taggers and select a flair tagger to augment the stylometric feature set published by @xcite . using predicted pos tags, we first reduce dependency on hand-engineered heuristics in feature calculations to gain more complete pos counts, increasing recall by 0.32 for conjunctions and 0.67 for prepositions when comparing pos-augmented features to their original counterparts. second, we disambiguate polysemous words such as cum, ut, and quod, increasing f1 score from 0.69 to 0.94 for cum clause identification and from 0.74 to 0.80 for relative clause identification. finally, we calculate newly enabled features including pos n-gram frequencies. we then train a random forest classifier to distinguish verse from prose, and through feature importance analysis we demonstrate that pos-augmented and pos n-gram features in particular quantify stylometric qualities highly relevant to genre classification. in these ways, we apply advances in latin nlp to literary critical questions regarding generic style. more generally, we showcase a methodology for latin that we hope will inform the quantitative criticism of other premodern languages as well.