Essential steps include removing diacritics, normalization, tokenization, stop-word removal, and morphological analysis to extract roots or stems.
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.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. To help you further, are you focusing on: applications (e
Arabic discourse frequently employs specific linguistic markers, such as the frequent use of the "Wa" (and) connector, which impacts how information is structured in large text chunks. To help you further, are you focusing on:
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