Enhanced Entity Recognition of Islamic Hadiths based-on Hybrid LSTM and AraBERT Model

This paper focuses on the training, evaluation and development of named entity recognition (NER) models designed for Islamic hadiths in Arabic Utilizing the Hadith Noor dataset, the study uses the BIO (Basic, In, Out) tagging scheme to classify words or tokens in NER tasks and the segmentation of the text into individual tokens. The right-skewed distribution revealed by examining the lengths of the Islamic hadiths revealed a right-skewed distribution, indicating that shorter texts are more common. Texts less than 100 words were most prevalent, followed by texts between 100 and 200 words, while texts longer than 200 words were rare. The dataset identifies eight types of entities, such as common names among narrators and locations. The study by training the three models AraBERT, LSTM and the hybrid model AraBERT-LSTM on Arabic text processing respectively, the hybrid model showed a performance, efficiency and accuracy of 0.981, outperforming the rest of the models, confirming its worth and reliability in NER tasks for natural language in Arabic, especially Islamic hadiths, which opens the way for exploring further investigations for future research in natural language processing.