Many contemporary Arabic texts are written without diacritics (vowels), causing the same word to be spelled in multiple ways, which creates challenges for automatic processing systems, including topic identification.
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 Arabic.doi
There is a significant gap between Modern Standard Arabic (MSA) used in formal writing and various spoken Arabic dialects (AD), requiring specialized models for each, especially since colloquial dialects are often used in social media datasets. Techniques for Arabic Topic Identification Let me know which direction you are interested in
approaches (e.g., algorithms, BERT, datasets)? often combined with triggers (i.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:
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.