O dy C y – A general-purpose NLP pipeline for A ncient G reek (2023)

Abstract: This paper presents a general-purpose NLP pipeline that achieves state-of-the-art performance on the Ancient Greek Perseus UD Treebank for several tasks (POS Tagging, Morphological Analysis and Dependency Parsing), and close to state-of-the-art performance on the Proiel UD Treebank. Our aim is to provide a reproducible, open source language processing pipeline for Ancient Greek, capable of handling input texts of varying quality. We measure the performance of our model against other comparable tools and then evaluate lemmatization errors.

## introduction the impact of digitization on literature research in contemporary english and other languages cannot be exaggerated. computational linguistics and natural language processing (nlp) have developed numerous tools that automate annotation and analysis that would otherwise have taken lifetimes of manual labor. similar advances have not been made for historical and low-resource language areas, for instance, classical literature in greek, latin, and hebrew. computational studies of classical literature are limited not only by fewer tools but also paywalls and licensed access (ex. loeb classical library and thesaurus linguae graecae), altogether complicating the training of neural-based language technology. to remedy this and to contribute to a relatively small number of existing nlp resources in this domain, we present a generalpurpose nlp pipeline for early forms of greek that will enable a computationally assisted analysis of, among other things, early forms of greek literature. ## results when evaluated on the ud perseus treebank, our model achieves state-of-the-art performance in pos tagging, morphological analysis, and dependency parsing (see ## conclusion comparing the performance of our model and several other comparable tools suggests there is a considerable amount of transferable information between ud perseus and ud proiel -the two most commonly used datasets for modeling linguistic features in ancient greek. we improved the stateof-art on some tasks, but more work is necessary to enhance the interoperability between the two datasets. we identify the main issues and list suggestions for improvement. resolving these issues is a good way of addressing the low generalizability of ancient greek language models on out-oftraining set samples (e.g., the bad performance of proiel models on perseus data). our best-performing model, odycy joint , comes with its own set of problems (see limitations), but comparing its performance to similar tools suggests it generalizes better across the two datasets. this is advantageous when analyzing mixed corpora, where it is unclear whether the corpus to be analyzed is more perseus-like or proiel-like. however, it should be noted that a better solution exists for proiel-like corpora, namely the grecy proiel model. finally, the model and its source code have been made open source foot_4 , together with the source code for evaluating the performance of ancient greek nlp tools foot_5 .