Building CorefLat. A linguistic resource for coreference and anaphora resolution in Latin
Authors: Eleonora Delfino, Roberta G. Leotta, Marco Carlo Passarotti, Giovanni Moretti (Università di Udine, Università Cattolica del Sacro Cuore, 2024)
Topic: Latin Resources

Abstract
This paper presents the initial stages of a project focused on coreference and anaphora resolution in Latin texts. By building a corpus enhanced with coreference/anaphora annotation, the project wants to explore empirically a layer of metalinguistic analysis that has not been yet extensively investigated in linguistic resources and natural language processing for Latin. After reviewing the related work, the paper discusses annotation criteria and data analysis, providing examples about a few issues that emerged during the annotation process.

Introduction
Over the past decade, research on linguistic resources and natural language processing (NLP) for Latin has seen remarkable growth. As for NLP tools, see the three editions of the evaluation campaign EvaLatin (last edition: ).}. However an important layer of metalinguistic annotation such as coreference and anaphora resolution still remains quite neglected. Indeed, except for the (meta)data produced by the FIR-2013 project Development and Integration of Advanced Linguistic Resources for Latin AUTHOR, there are neither corpora enhanced with coreferential/anaphoric annotations nor NLP tools for automatic coreference/anaphora resolution for Latin. This absence limits the degree of granularity of information extraction from Latin corpora. Such a limitation is particularly compelling, as Latin texts are mainly used for purposes of research in the Humanities, like literary, stylistic and philosophical analysis. To give an example, investigating in Latin texts a philosophical concept conveyed by a word like voluntas `will', or studying the turns of a certain character in a drama would highly benefit from a textual resource where, for instance, the ana-/cataphoric references of pronouns are resolved. The PRIN 2022 project Textual Data and Tools for Coreference Resolution of Latin was granted funding to overcome such situation. Run jointly by the Università Cattolica of Milan and the University of Udine, the project stems from the FIR-2013 pilot experience, having the short-term objective of developing a large-scale and balanced dataset of Latin texts enhanced with coreference/anaphora resolution (called CorefLat). Based upon this annotated dataset, the project has two long-term objectives. The first aims to develop and evaluate a set of trained models for automatic coreference/anaphora resolution of Latin. The second long-term objective wants to publish the metadata pertaining to coreference/anaphora resolution as Linked Data, to make them interoperable with other (meta)data in the Web. To this aim, the texts of the annotated dataset are selected among those published in the LiLa Knowledge Base, a collection of multiple linguistic resources for Latin described with the same knowledge vocabulary and interconnected according to the principles of the Linked Data paradigm AUTHOR}. This paper details the initial stages of the creation of the CorefLat annotated dataset.

Conclusion
In this paper, we provided a overview of a project aimed to build a Latin corpus enhanced with coreference and anaphora resolution. Specifically, we delineated the guidelines employed in the annotation process. We exemplified a few annotation challenges, highlighting how this task necessitates a profound interaction among various fields of expertise, including linguistics, textual criticism, and literature. Moreover, once a sufficiently large dataset will be available, NLP will be concerned, as we plan to use the annotated data to train and evaluate a model to perform automatic CR/AR of Latin. In the long term, a follow-up of the project will have to encompass other types of coreferences, such as appositive, predicative, and descriptive ones. Additionally, given that Latin is a pro-drop language, ellipsis resolution is a natural next step that, together with CR and CR, promises to provide a deep level of granularity in extracting information from Latin texts.