A review of Chinese named entity recognition

• Jieren Cheng,
• Jingxin Liu,
• Xinbin Xu,
• Dongwan Xia,
• Le Liu,
• Victor S. Sheng,

Vol. 15, No. 6, June 30, 2021
10.3837/tiis.2021.06.004, Download Paper (Free):

• Chinese word segmentation
• Deep Learning
• Machine Learning
• Model framework
• named entity recognition

Abstract

Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

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