Semi-supervised 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 patients' 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 question-answer pairs and drug instructions from the internet and design a co-training style method (which is a semi-supervised 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.