Exploring Neural Topic Modeling on a Classical Latin Corpus

Ginevra Martinelli, Paola Impicciché, Elisabetta Fersini, Francesco Mambrini, Marco Passarotti

Abstract
The large availability of processable textual resources for Classical Latin has made it possible to study Latin literature through methods and tools that support distant reading. This paper describes a number of experiments carried out to test the possibility of investigating the thematic distribution of the Classical Latin corpus Opera Latina by means of topic modeling. For this purpose, we train, optimize and compare two neural models, Product-of-Experts LDA (ProdLDA) and Embedded Topic Model (ETM), opportunely revised to deal with the textual data from a Classical Latin corpus, to evaluate which one performs better both on the basis of topic diversity and topic coherence metrics, and from a human judgment point of view. Our results show that the topics extracted by neural models are coherent and interpretable and that they are significant from the perspective of a Latin scholar. The source code of the proposed model is available at https://github.com/MIND-Lab/LatinProdLDA.
Anthology ID:
2024.lrec-main.606
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
6929–6934
Language:
URL:
https://aclanthology.org/2024.lrec-main.606
DOI:
Bibkey:
Cite (ACL):
Ginevra Martinelli, Paola Impicciché, Elisabetta Fersini, Francesco Mambrini, and Marco Passarotti. 2024. Exploring Neural Topic Modeling on a Classical Latin Corpus. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 6929–6934, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Exploring Neural Topic Modeling on a Classical Latin Corpus (Martinelli et al., LREC-COLING 2024)
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