---
license: mit
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
´´´´

```bibtex

@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.

# Contact
[Felermino Ali](https://felerminoali.github.io/)

[github](https://github.com/felerminoali/emakhuwa-nlp/tree/master/datasets/loanwords)