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Learning a historical language is different from learning a modern language in view of the emphasis on the work on texts instead of everyday communication. Therefore, not only the expectations and motivation differ, but also the teaching methodology. Whereas learners of modern languages focus on language production, learners of Latin read or translate their texts. Because of the overall low frequency of occurrence of a Latin word or a phrase in this kind of learning environment, most students are often unfamiliar with a given word and therefore finally unable to translate the texts. To tackle this underlying problem of Latin classes, an interdisciplinary research project conducted different studies using a data-driven learning (DDL) approach. So far, the findings are very multifaceted and sometimes even surprising: the majority of students fail to lemmatize words correctly even though they have learned Latin for four years or more.

Konstantin Schulz shows various applications of natural language processing (NLP) to the field of Classics, especially to Latin texts. He addresses different levels of linguistic analysis while also highlighting educational benefits and important theoretical pitfalls, especially in vocabulary learning. NLP can solve some problems reasonably well, like tailoring exercises to the learners' current state of knowledge. However, some tasks still prove to be too difficult for machines at the moment, e.g. reliable and highly accurate parsing of syntax for historical languages.

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