ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2021.016054 Article Arabic Named Entity Recognition: A BERT-BGRU Approach Norah Alsaaran * and Maha Alrabiah Department of Computer Science, Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia * Corresponding Author: Norah Alsaaran. Email: NSAlsaaran@imamu.edu.sa Received: 20 December 2020; Accepted: 20 January 2021 Abstract: Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to locate, extract, and classify named entities into a predefned category such as person, organiza- tion and location. Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources, which is time consuming and not adequate for resource-scarce languages such as Arabic. Recently, deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features. In addition, transfer learning has also proven its effciency in several NLP tasks by exploiting pretrained language models that are used to transfer knowl- edge learned from large-scale datasets to domain-specifc tasks. Bidirectional Encoder Representation from Transformer (BERT) is a contextual language model that generates the semantic vectors dynamically according to the con- text of the words. BERT architecture relay on multi-head attention that allows it to capture global dependencies between words. In this paper, we propose a deep learning-based model by fne-tuning BERT model to recognize and classify Arabic named entities. The pre-trained BERT context embeddings were used as input features to a Bidirectional Gated Recurrent Unit (BGRU) and were fne-tuned using two annotated Arabic Named Entity Recognition (ANER) datasets. Experimental results demonstrate that the proposed model outperformed state-of-the-art ANER models achieving 92.28% and 90.68% F-measure values on the ANERCorp dataset and the merged ANERCorp and AQMAR dataset, respectively. Keywords: Named entity recognition; Arabic; deep learning; BGRU; BERT 1 Introduction Textual information represents a wide share of digital content, and it is continuing to grow rapidly every moment, which requires linguistic and deep semantic analysis techniques to achieve a better and faster understanding of this information. NER is one of the techniques used to identify and classify each word in a given text into predefned semantic categories such as person name, location name, organization name, and miscellaneous. NER plays an essential role in several NLP tasks such as information retrieval, question answering, machine translation, and text summarization. However, NER is considered a challenging task and has several dif fculties. For This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.