Our team SRCB participated in the Social Media Adverse Drug Event Detection(SM-ADE) subtask of NTCIR-17 Medical Natural Language Processing for Social media and Clinical texts (MedNLP-SC). The task focuses on solving the problem of Adverse Drug Event (ADE) detection for social media texts in Japanese, English, German, and French, which is a multi-labeling problem aimed at expressing the positive or negative status as an ADE for 22 symptom labels respectively. In this paper, we report our approaches which can be mainly categorized into 3 types according to which task we cast the original task to, including multi-label classification, binary classification and joint entity and relation extraction. Besides, we also conduct optimizations on the approaches that rely on pre-trained transformer language models, with the support of various techniques such as continual pretraining, gradient boosting methods, and transfer learning.
LI et al. (Tue,) studied this question.
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