| Sumario: | Digital-assisted intertextuality analysis often yields large amounts of results, many of which are irrelevant to researchers from a hermeneutic point of view. One strategy for minimizing these hermeneutically non meaningful findings is to apply a filter that sorts the results based on specified parts-of-speech. Building on Mare Revellio's historical text-reuse grammar (HTRG) we demonstrate that an implementation of such a filter accommodating the hermeneutical context proves to be advantageous. We assessed the performance of various Latin part-of-speech (POS) taggers to refine our filtering process, using evaluation data on text congruencies from the Latin authors Virgil and Jerome. Among the Classical Language Toolkit , the TreeTagger , the Cracovia system , LatinCy , and ChatGPT , the Cracovia system surpassed the other taggers by approximately 2 percentage points in accuracy. While this tagger leverages transformer-based machine learning algorithms, the older, probabilistic-based TreeTagger demonstrated competitive performance. Although GPT-4 showed remarkable results, it still lags behind the state-of-the-art taggers for Latin. In order to build a powerful digital citation detection tool for intertextual relationships in ancient texts, the most accurate analysis of POS is crucial in filtering and evaluating valid citations.
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