Essential steps include removing diacritics, normalization, tokenization, stop-word removal, and morphological analysis to extract roots or stems.
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 Challenges in the Field Arabic is derived from
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. For example, a single word can include affixes
Arabic has high derivational and inflectional complexity. For example, a single word can include affixes (prefixes, suffixes, infixes) that represent pronouns, conjunctions, and prepositions.
Techniques like Term Frequency-Inverse Document Frequency (TFIDF) and k-Nearest Neighbors (kNN) are used, often combined with triggers (i.e., Average Mutual Information) to improve results.
Arabic dialects vary significantly across 22 countries, creating difficulties in developing universal models, often necessitating country-specific or dialectal classification methods.