You-Chen Zhang


ISLab System for SMM4H Shared Task 2020
Chen-Kai Wang | Hong-Jie Dai | You-Chen Zhang | Bo-Chun Xu | Bo-Hong Wang | You-Ning Xu | Po-Hao Chen | Chung-Hong Lee
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

In this paper, we described our systems for the first and second subtasks of Social Media Mining for Health Applications (SMM4H) shared task in 2020. The two subtasks are automatic classi-fication of medication mentions and adverse effect in tweets. Our systems for both subtasks are based on Robustly optimized BERT approach (RoBERTa) and our previous work at SMM4H’19. The best F1-scores achieved by our systems for subtask 1 and 2 were 0.7974 and 0.64 respec-tively, which outperformed the average F1-scores among all teams’ best runs by at least 0.13.

Cancer Registry Information Extraction via Transfer Learning
Yan-Jie Lin | Hong-Jie Dai | You-Chen Zhang | Chung-Yang Wu | Yu-Cheng Chang | Pin-Jou Lu | Chih-Jen Huang | Yu-Tsang Wang | Hui-Min Hsieh | Kun-San Chao | Tsang-Wu Liu | I-Shou Chang | Yi-Hsin Connie Yang | Ti-Hao Wang | Ko-Jiunn Liu | Li-Tzong Chen | Sheau-Fang Yang
Proceedings of the 3rd Clinical Natural Language Processing Workshop

A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and F-measure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.