Empowering Low-Resource Regional Languages with Lexicons : A Comparative Study of NLP Tools for Morphosyntactic Analysis

Cristina Garcia Holgado, Marianne Vergez-Couret

Abstract
We investigate the effect of integrating lexicon information to an extremely low-resource language when annotated data is scarce for morpho-syntactic analysis. Obtaining such data and linguistic resources for these languages are usually constrained by a lack of human and financial resources making this task particularly challenging. In this paper, we describe the collection and leverage of a bilingual lexicon for Poitevin-Saintongeais, a regional language of France, to create augmented data through a neighbor-based distributional method. We assess this lexicon-driven approach in improving POS tagging while using different lexicon and augmented data sizes. To evaluate this strategy, we compare two distinct paradigms: neural networks, which typically require extensive data, and a conventional probabilistic approach, in which a lexicon is instrumental in its performance. Our findings reveal that the lexicon is a valuable asset for all models, but in particular for neural, demonstrating an enhanced generalization across diverse classes without requiring an extensive lexicon size.
Anthology ID:
2024.lrec-main.510
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:
5747–5756
Language:
URL:
https://aclanthology.org/2024.lrec-main.510
DOI:
Bibkey:
Cite (ACL):
Cristina Garcia Holgado and Marianne Vergez-Couret. 2024. Empowering Low-Resource Regional Languages with Lexicons : A Comparative Study of NLP Tools for Morphosyntactic Analysis. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5747–5756, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Empowering Low-Resource Regional Languages with Lexicons : A Comparative Study of NLP Tools for Morphosyntactic Analysis (Garcia Holgado & Vergez-Couret, LREC-COLING 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.510.pdf
PDF Search