An intelligent deep learning for Arabic stemming and morphological classification

Abstract

Arabic language, due to its complex morphology and richness of grammar features poses significant challenges in natural language processing (NLP). In this paper, we propose a two-stage deep learning pipeline that combines Arabic text stemming and morphological classification within a single deep learning architecture. The relationship between morphological reduction and grammatical categorization is exploited by combining character-level sequence processing with transformer-based classification. A bidirectional long short-term memory (Bi-LSTM) model is employed for Arabic stem extraction to build a sequence-to-sequence (seq2seq) stemming model named Char Stemmer. To evaluate the proposed model, a gold standard dataset consisting of 260,000 traditional Arabic words extracted from Quranic words and classical Arabic books is utilized. This dataset contains a wide range of challenging word structures suitable for robust evaluation. The Char Stemmer achieved an accuracy of 93.88% on the stemming task. The proposed model obtained 93.88% accuracy, demonstrating a 38% improvement over the best traditional stemmer, P-Stemmer. Beyond stemming, the impact of stemmers on subsequent tasks is evaluated, particularly Arabic word classification. Words are categorized into three morphological classes: noun, verb, and particle. Experimental results show that the proposed system achieved macro average precision, recall, and F1-score of 0.91, 0.89, and 0.90, respectively, with an overall classification accuracy of approximately 99%.