Update README.md

README.md

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-license: cc-by-4.0
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+---
+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](https://github.com/csu-signal/loan-word-detection) implementation.
+
+# Contact
+[Felermino Ali](https://felerminoali.github.io/)
+[github](https://github.com/felerminoali/emakhuwa-nlp/tree/master/datasets/loanwords)