中文醫療文件的命名實體辨識報告

MIGBaseline at ROCLING 2022 Shared Task: Report on Named Entity Recognition Using Chinese Healthcare Datasets

命名實體（Named Entity Recognition , NER）工具發展已久，但少有針對醫療專業領域的NER工具，因此建立一個適用於醫療文件的NER工具是至關重要的。本研究使用了在中英任務中表現出色的W2NER模型，藉由更改資料的輸入、選用不同的預訓練語言模型以及運用不同的訓練策略，建立一個適合於中文醫療資料集的NER模型。我們的最佳模型在該資料集獲得81.93%的F1分數，並在ROCLING 2022 NER競賽(Lee et al., 2022)中排名第一。

Named Entity Recognition （NER） tools have been in development for years, yet few have been aimed at medical documents. The increasing needs for analyzing medical data makes it crucial to build a sophisticated NER model for this missing area. In this paper, W2NER, the state-of-the-art NER model, which has excelled in English and Chinese tasks, is run through selected inputs, several pretrained language models, and training strategies. The objective was to build an NER model suitable for healthcare corpora in Chinese. The best model managed to achieve an F1 score at 81.93%, which ranked first in the ROCLING 2022 shared task.