Research Progress on Named Entity Recognition in Chinese Deep Learning

Chinese named entity recognition（CNER）is the process of identifying and categorizing entities with specific meanings in Chinese text. It is a crucial component in many downstream tasks within natural language processing. In the past few years, deep learning technology has increasingly relied on end-to-end methods to automatically learn more complex and abstract data features, thereby reducing the need for manual annotation and addressing the issue of data sparsity in high-dimensional feature spaces. As a result, deep learning has emerged as the dominant approach for Chinese named entity recognition. This article initially provides an overview of the historical development of named entity recognition and outlines the specific challenges and intricacies associated with Chinese named entity recognition（CNER）. It then delves into the distinct processing characteristics of CNER and categorizes deep learning-based methods for CNER into three key areas：flat entity boundary problem, Chinese nested named entity recognition, and CNER small sample problem. The paper offers a detailed description of the models, subdivisions, and recent research progress in each of these areas, and presents experimental results of several noteworthy deep learning methods on relevant datasets. Finally, the article identifies the challenges and future research directions for CNER, and concludes with a summary of commonly used datasets and evaluation methods for Chinese named entity recognition. © 2016 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.