FastSpell: The LangId Magic Spell
Marta Bañón, Gema Ramírez-Sánchez, Jaume Zaragoza-Bernabeu, Sergio Ortiz Rojas
Abstract
Language identification is a crucial component in the automated production of language resources, particularly in multilingual and big data contexts. However, commonly used language identifiers struggle to differentiate between similar or closely-related languages. This paper introduces FastSpell, a language identifier that combines fastText (a pre-trained language identifier tool) and Hunspell (a spell checker) with the aim of having a refined second-opinion before deciding which language should be assigned to a text. We provide a description of the FastSpell algorithm along with an explanation on how to use and configure it. To that end, we motivate the need of such a tool and present a benchmark including some popular language identifiers evaluated during the development of FastSpell. We show how FastSpell is useful not only to improve identification of similar languages, but also to identify new ones ignored by other tools.
BibTeX
@inproceedings{banon-etal-2024-fastspell,
title = "{F}ast{S}pell: The {L}ang{I}d Magic Spell",
author = "Ba{\~n}{\'o}n, Marta and
Ram{\'i}rez-S{\'a}nchez, Gema and
Zaragoza-Bernabeu, Jaume and
Ortiz Rojas, Sergio",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.626/",
pages = "7133--7140"
}