Detecting loanwords in Emakhuwa: an extremely low-resource bantu language exhibiting significant borrowing from portuguese

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Ali, Felermino (Author) Other; Cardoso, Henrique Lopes (Author) FEUP; Sousa-Silva, Rui (Author) FLUP

Conference proceedings International

Title: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Pages: 14750-4759

Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Torino, 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-010-KZT

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific