Semisupervised learning for classification on Chinese drug treatment questions Construction of automatic Question Answering (QA) system for online healthcare community gains great attention from the researchers due to the lack of practitioners who can respond precise answers to patientsu0027 consultations. In this paper, we focus on drug treatment question classification task, which can help building QA system for such questions. Due to the lack of labeled data and the high cost of labeling work, we consider using documents or texts from the internet which are related to our task. We fetch a large amount of unlabeled questionanswer pairs and drug instructions from the internet and design a cotraining style method (which is a semisupervised learning method) to utilize them. Using some specified models as classifiers, we prove that the classifier trained under our method outperforms the same model trained under supervised learning with less labeled data as input.

Question Answering based Clinical Text Structuring Using Pretrained Language Model Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific endtoend models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QACTS) task to unify different specific CTS tasks and make dataset shareable. A novel model that aims to introduce domainspecific features (e.g., clinical named entity information) into pretrained language model is also proposed for QACTS task. Experimental results on Chinese pathology reports collected from Ruijing Hospital demonstrate our presented QACTS task is very effective to improve the performance on specific tasks. Our proposed model also competes favorably with strong baseline models in specific tasks.