基于深度学习的命名实体识别研究综述

Survey of named entity recognition research based on deep learning

命名实体识别是自然语言处理领域的一项关键任务,其目的在于从自然语言文本中识别出具有特定含义的实体,如人名、地名、机构名和专有名词等。在命名实体识别任务中,研究人员提出过多种方法,包括基于知识和有监督的机器学习方法。近年来,随着互联网文本数据规模的快速扩大和深度学习技术的快速发展,深度学习模型已成为命名实体识别的研究热点,并在该领域取得显著进展。文中全面回顾现有的命名实体识别深度学习技术,主要分为四类:基于卷积神经网络模型、基于循环神经网络模型、基于Transformer模型和基于图神经网络模型的命名实体识别。此外,对深度学习的命名实体识别架构进行了介绍。最后,探讨命名实体识别所面临的挑战以及未来可能的研究方向,以期推动命名实体识别领域的进一步发展。

Named entity recognition is a crucial task in the field of Natural Language Processing,which aims to identify entities with specific meanings from natural language texts,such as person names,place names,institution names,and proper nouns.In the task of named entity recognition,researchers have proposed various methods,including those based on domain knowledge and supervised machine learning approaches.In recent years,with the rapid expansion ofinternet text data and the rapid development of deep learning techniques,deep learning models have become aresearch hotspot in named entity recognition and have made significant progress in this field.A comprehensive review of existing deep learning techniques for named entity recognition is provided,categorizing them into four main categories:models based on convolutional neural networks(CNN),recurrent neural networks(RNN),Transformer models,and graph neural networks(GNN) for NER.An overview of deep learning architectures for named entity recognition is presented.The challenges faced by named entity recognition and potential research directions in the future are explored to promote further development in the field of named entity recognition.