Named Entity Recognition for Arabic Medical Texts Using Deep Learning Models

Named Entity Recognition (NER) plays a vital role in extracting meaningful information from textual data in the medical domain. This paper focuses on NER for Arabic medical texts, specifically targeting the recognition of disease entities. The study presents a comparative analysis of deep learning techniques, including Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), LSTM-CRF, and Bidirectional LSTM (BiLSTM), applied to a dataset comprising Arabic medical texts related to diseases. The dataset is meticulously annotated, ensuring accurate labelling of disease entities for training and evaluation purposes. The models are trained and evaluated using appropriate loss functions and evaluation metrics, such as precision, recall, and F1-score. Comparative experiments are conducted to assess the performance of each model on the disease dataset. The results demonstrate the effectiveness of deep learning techniques for NER in Arabic medical texts, with the LSTM-CRF and BiLSTM-CRF models outperforming the standalone CRF and LSTM models. LSTM-CRF and BiLSTM-CRF models reported F1-score of 0.97 and 0.94. These hybrid models achieve higher precision, recall, and F1-score, showcasing their ability to accurately identify disease entities in Arabic medical texts. The findings of this study contribute to the advancement of NER techniques for Arabic medical texts, focusing on disease entities. The comparative analysis of CRF, LSTM, LSTM-CRF, and BiLSTM models provides valuable insights into their respective strengths and limitations of NER for Arabic medical texts. These insights can guide the selection and implementation of appropriate models for disease entity recognition in Arabic medical texts, facilitating accurate information extraction and analysis in the medical domain.