Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), and its significance is amplified in the biomedical field, especially for resource-constrained languages like Arabic. This study proposes a rule-based system for the identification of disease-related entities in Arabic biomedical texts. The method accurately finds relevant terms by using dictionary matching along with a set of linguistic rules. The system uses fuzzy matching based on the Levenshtein similarity measure to deal with the common changes in spelling and terminology. This lets it find entities that are spelled differently but are still the same. The model achieved strong evaluation results, with an F1-score of 90.8%, a recall of 90.8%, and a precision of 90.7%. These outcomes demonstrate that competitive performance can be achieved through a well-structured rule-based framework enhanced by similarity techniques, without the need for advanced machine learning methods or external resources.
Eldouby et al. (Thu,) studied this question.
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