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Arabic Named Entity Recognition:A BERT-BGRU Approach

Computers, Materials & Continua 2021年第7期68卷 471-485页

作者： norah alsaaran Maha Alrabiah Department of Computer Science Imam Muhammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia

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 predefined category such as person,organization and *** 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 ***,deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted *** addition,transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models that are used to transfer knowledge learned from large-scale datasets to domain-specific *** Encoder Representation from Transformer(BERT)is a contextual language model that generates the semantic vectors dynamically according to the context of the *** architecture relay on multi-head attention that allows it to capture global dependencies between *** this paper,we propose a deep learning-based model by fine-tuning BERT model to recognize and classify Arabic named *** pre-trained BERT context embeddings were used as input features to a Bidirectional Gated Recurrent Unit(BGRU)and were fine-tuned using two annotated Arabic Named Entity Recognition(ANER)*** 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.

关键词： Named entity recognition Arabic deep learning BGRU BERT

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