Word Representation Models for Arabic Dialect Identification
Mahmoud Sobhy, Ahmed H. Abu El-Atta, Ahmed El-Sawy, Hamada Nayel · 2022
This paper describes the systems submitted by BFCAI team to Nuanced Arabic Dialect Identification (NADI) shared task 2022.Dialect identification task aims at detecting the source variant of a given text or speech segment automatically.There are two subtasks in NADI 2022, the first subtask for country-level identification and the second subtask for sentiment analysis.Our team participated in the first subtask.The proposed systems use Term Frequency Inverse/Document Frequency and word embeddings as vectorization models.Different machine learning algorithms have been used as classifiers.The proposed systems have been tested on two test sets: Test-A and Test-B.The proposed models achieved Macro-f1 score of 21.25% and 9.71% for Test-A and Test-B set respectively.On other hand, the best-performed submitted system achieved Macro-f1 score of 36.48% and 18.95% for Test-A and Test-B set respectively.