Abstract
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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Notes
- 1.
The annotated dataset has not been deposited in a public repository but is available to the research community under data use agreements from the corresponding author on request.
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Zhang, J., Li, J., Jiao, Z., Yan, J. (2019). Overview of CCKS 2018 Task 1: Named Entity Recognition in Chinese Electronic Medical Records. In: Zhu, X., Qin, B., Zhu, X., Liu, M., Qian, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding. CCKS 2019. Communications in Computer and Information Science, vol 1134. Springer, Singapore. https://doi.org/10.1007/978-981-15-1956-7_14
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