计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (16): 34-48.DOI: 10.3778/j.issn.1002-8331.2312-0260
肖蕾,陈镇家
出版日期:
2024-08-15
发布日期:
2024-08-15
XIAO Lei, CHEN Zhenjia
Online:
2024-08-15
Published:
2024-08-15
摘要: 中文实体抽取(Chinese named entity recognition,CNER)是中文信息抽取任务中的关键一步,是问答系统、机器翻译和知识图谱等下游任务的基础,其方法主要分为知识驱动和数据驱动两大类。然而基于规则、词典与机器学习的传统知识驱动方法存在忽视上下文语义信息、计算成本高和低召回率的问题,限制了CNER技术的发展。介绍了CNER的定义和发展历程。详细整理了CNER任务的典型数据集、训练工具、序列标注方式和模型评价指标。对基于数据驱动的方法进行了总结,将数据驱动的方法划分为基于深度学习、预训练语言模型和中文实体关系联合抽取等方法,并分析了数据驱动方法在不同领域的实际应用场景。对CNER任务的未来研究方向进行了展望,为新方法的提出提供一定参考。
肖蕾, 陈镇家. 数据驱动的中文实体抽取方法综述[J]. 计算机工程与应用, 2024, 60(16): 34-48.
XIAO Lei, CHEN Zhenjia. Review of Data-Driven Approaches to Chinese Named Entity Recognition[J]. Computer Engineering and Applications, 2024, 60(16): 34-48.
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