Chinese Healthcare Knowledge Base (中文健康照護知識庫)

  • Goals: 
  1. Developing NLP techniques (including entity recognition, entity linking, entity-relationship extraction, open information extraction, etc.) to automatically acquire Chinese healthcare knowledge.​
  2. Incorporating domain knowledge to develop health-related intelligent applications
  3. Visualizing the Chinese healthcare knowledge base as the knowledge graph.  
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  • Achievements: 
  1. Lung-Hao Lee*, and Yi Lu (2021). Multiple Embeddings Enhanced Multi-Graph Neural Networks for Chinese Healthcare Named Entity Recognition. IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), 25(7), 2801- 2810. (SCIE) [DOI] #Method #Data
  2. Lung-Hao Lee, Tzu-Mi Lin and Chao-Yi Chen (2023). Overview of the ROCLING 2023 Shared Task for Chinese Multi-genre Named Entity Recognition in the Healthcare Domain. In Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING'23), Taipei, Taiwan, 20-21 October, 2023, pp. 333-338. [PDF] #Task #Data
  3. Lung-Hao Lee, Chao-Yi Chen, Liang-Chih Yu, and Yuen-Hsien Tseng (2022). Overview of the ROCLING 2022 Shared Task for Chinese Healthcare Named Entity Recognition. In Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING'22), Taipei, Taiwan, 21-22 November, 2022, pp. 363-368. [PDF] #Task #Data
  4. Tzu-Mi Lin, Chao-Yi Chen, Yu-Wen Tzeng, and  Lung-Hao Lee (2022). NCUEE-NLP@SMM4H'22: Classification of Self-reported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models. In Proceedings of the 7th Social Media Mining for Health Applications (SMM4H'22) Workshop and Shared Tasks, Gyeongju, Republic of Korea, 17 October, 2022, pp. 62-64. [PDF] #Applications
  5. Lung-Hao Lee, Chien-Huan Lu, and Tzu-Mi Lin (2022). NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval'22), Seattle, Washington and Online, 14-15 July, 2022, pp. 1597-1602. [PDF] #Method
  6. Lung-Hao Lee, Man-Chen Hung, Chien-Huan Lu, Chang-Hao Chen, Po-Lei Lee, and Kuo-Kai Shyu (2021). Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers. In Proceedings of the 6th Social Media Mining for Health Applications (SMM4H'21) Workshop and Shared Tasks, Online, 10 June, 2021, pp. 98-101. [PDF] #Applications
  7. Lung-Hao Lee, Po-Han Chen, Hao-Chuan Kao, Ting-Chun Hung, Po-Lei Lee, and Kuo-Kai Shyu (2020). Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees. In Proceedings of the 5th Social Media Mining for Health Applications (SMM4H'20) Workshop and Shared Tasks, Online, 12 December, 2020. pp. 131-133. [PDF]  #Applications
  8. 盧毅、李龍豪* (2020)。基於圖神經網路之中文健康照護命名實體辨識。中文計算語言學期刊。第25卷，第2期 (Special Issue on Selected Papers from ROCLING XXXII)，2020年12月，第21-36頁。(THCI) [PDF]
  9. 盧毅、李龍豪 (2020年9月)。門控圖序列神經網路之中文健康照護命名實體辨識。第32屆自然語言與語音處理研討會(ROCLING'20)。台北：國立臺北科技大學。(最佳論文獎)​​
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  • Data:​​  ​
  1. Chinese HealthNER Corpus   https://github.com/NCUEE-NLPLab/Chinese-HealthNER-Corpus
  2. ROCLING-2022 CHNER Dataset  https://github.com/NCUEE-NLPLab/ROCLING-2022-ST-CHNER​
  3. ROCLING-2023 MultiNER-Health Dataset https://github.com/NCUEE-NLPLab/ROCLING-2023-ST-MultiNERHealth​​