Datasets:
license: cc-by-4.0
language:
- pt
- vmw
task_categories:
- text-classification
Detecting Loanwords in Emakhuwa
Paper: Detecting Loanwords in Emakhuwa: An Extremely Low-Resource {B}antu Language Exhibiting Significant Borrowing from {P}ortuguese ´´´´ @inproceedings{ali-etal-2024-detecting, title = "Detecting Loanwords in Emakhuwa: An Extremely Low-Resource {B}antu Language Exhibiting Significant Borrowing from {P}ortuguese", author = "Ali, Felermino Dario Mario and Lopes Cardoso, Henrique and Sousa-Silva, Rui", 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.425", pages = "4750--4759", abstract = "The accurate identification of loanwords within a given text holds significant potential as a valuable tool for addressing data augmentation and mitigating data sparsity issues. Such identification can improve the performance of various natural language processing tasks, particularly in the context of low-resource languages that lack standardized spelling conventions.This research proposes a supervised method to identify loanwords in Emakhuwa, borrowed from Portuguese. Our methodology encompasses a two-fold approach. Firstly, we employ traditional machine learning algorithms incorporating handcrafted features, including language-specific and similarity-based features. We build upon prior studies to extract similarity features and propose utilizing two external resources: a Sequence-to-Sequence model and a dictionary. This innovative approach allows us to identify loanwords solely by analyzing the target word without prior knowledge about its donor counterpart. Furthermore, we fine-tune the pre-trained CANINE model for the downstream task of loanword detection, which culminates in the impressive achievement of the F1-score of 93{%}. To the best of our knowledge, this study is the first of its kind focusing on Emakhuwa, and the preliminary results are promising as they pave the way to further advancements.", } ´´´
Licence
This project is released under the MIT license.
Acknowledgements
The base code is based on a previous implementation.