Abstract
In this research work we have especially studied both Modern Standard Arabic Language and Libyan Dialects. The focus of our study involved an in-depth analysis of both Modern Standard Arabic Language and Libyan Dialects through the lens of Natural Language Processing (NLP). Our primary objective was to assess the efficacy of a novel Machine Learning Model in enhancing performance and accurately categorizing the dataset for identifying Libyan dialects, while also addressing the preprocessing requirements of the diverse Libyan Dialects dataset. The core function of our developed Model was to transform the preprocessed Dialects dataset into its corresponding standard Arabic roots, recognizing that Libyan dialects are primarily spoken rather than written. Our identification model was specifically designed to navigate the challenges posed by these distinct Dialects, aiming to mitigate ambiguities that could potentially impact the model's effectiveness. The Arabic Libyan dialects identification model we proposed was tailored to leverage Natural Language Processing techniques to automatically determine the Arabic Libyan dialect present in a given text. This model aligns with the foundational principles of NLP, serving as a crucial initial step in a range of natural language processing applications such as machine translation, multilingual text-to-speech synthesis, and cross-language text generation. Our research paper provides a comprehensive overview of the Arabic Libyan dialects identification model, highlighting the utilization of feature representation techniques to train the proposed ML model effectively.