Detecting Sexual Content at the Sentence Level in First Millennium Latin Texts

Thibault Clerice


Abstract
In this study, we propose to evaluate the use of deep learning methods for semantic classification at the sentence level to accelerate the process of corpus building in the field of humanities and linguistics, a traditional and time-consuming task. We introduce a novel corpus comprising around 2500 sentences spanning from 300 BCE to 900 CE including sexual semantics (medical, erotica, etc.). We evaluate various sentence classification approaches and different input embedding layers, and show that all consistently outperform simple token-based searches. We explore the integration of idiolectal and sociolectal metadata embeddings (centuries, author, type of writing), but find that it leads to overfitting. Our results demonstrate the effectiveness of this approach, achieving high precision and true positive rates (TPR) of respectively 70.60% and 86.33% using HAN. We evaluate the impact of the dataset size on the model performances (420 instead of 2013 training samples), and show that, while our models perform worse, they still offer a high enough precision and TPR, even without MLM, respectively 69% and 51%. Given the result, we provide an analysis of the attention mechanism as a supporting added value for humanists in order to produce more data.
Anthology ID:
2024.lrec-main.427
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:
4772–4783
Language:
URL:
https://aclanthology.org/2024.lrec-main.427
DOI:
Bibkey:
Cite (ACL):
Thibault Clerice. 2024. Detecting Sexual Content at the Sentence Level in First Millennium Latin Texts. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4772–4783, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Detecting Sexual Content at the Sentence Level in First Millennium Latin Texts (Clerice, LREC-COLING 2024)
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PDF:
https://aclanthology.org/2024.lrec-main.427.pdf