While there is a growing number of Arabic NLP datasets, there is a lack of high-quality, large-scale, and diverse datasets for certain domains.
Support Vector Machines (SVM) have proven superior for Arabic topic classification compared to others. Arabic.doi
Recent advances include fine-tuning pre-trained language models like BERT (specifically AraBERT or Arabic BERT) to capture semantic context better than keyword-based approaches. Challenges in the Field While there is a growing number of Arabic
Arabic is derived from triconsonantal roots. Hundreds of distinct words can stem from a single root, making root-based stemming (finding the root) or lemmatization (finding the dictionary form) crucial for reducing vocabulary size and identifying topics. Challenges in the Field Arabic is derived from
Essential steps include removing diacritics, normalization, tokenization, stop-word removal, and morphological analysis to extract roots or stems.
applications (e.g., software tools, news classification)? Dialectal or Modern Standard Arabic? Let me know which direction you are interested in. (PDF) Arabic Topic Identification: A Decade Scoping Review