任务(项目管理)
计算机科学
北京
命名实体识别
特征工程
特征(语言学)
领域(数学)
人工智能
情报检索
自然语言处理
深度学习
工程类
中国
系统工程
纯数学
法学
哲学
语言学
数学
政治学
作者
Jiangtao Zhang,Juanzi Li,Zengtao Jiao,Jun Yan
出处
期刊:Communications in computer and information science
日期:2019-01-01
卷期号:: 158-164
被引量:5
标识
DOI:10.1007/978-981-15-1956-7_14
摘要
The CCKS 2018 presented a named entity recognition (NER) task focusing on Chinese electronic medical records (EMR). The Knowledge Engineering Group of Tsinghua University and Yidu Cloud Beijing Technology Co., Ltd. provided an annotated dataset for this task, which is the only publicly available dataset in the field of Chinese EMR. Using this dataset, 69 systems were developed for the task. The performance of the systems showed that the traditional CRF and Bi-LSTM model were the most popular models for the task. The system achieved the highest performance by combining CRF or Bi-LSTM model with complex feature engineering, indicating that feature engineering is still indispensable. These results also showed that the performance of the task could be augmented with rule-based systems to determine clinical named entities.
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