A Domain Knowledgexe2x80x94Enabled Hybrid SemiSupervision Learning Method Due to the advances in dramatically increased computing power, machine learning technologies have been widely exploited to provide promising solutions in different application fields. However, the high performance of many of these machine learning technologies highly relies on the availability of sufficient annotated data. Considering the fact that data annotation is an extremely timeconsuming process, this condition is not always practical. To address this challenge, in this paper we propose a novel computing method, called hybrid semisupervision machine learning, that exploits the loose domain knowledge to enable the accurate results even in the presence of limited labeled data. Simulations results illustrate the effectiveness of our method.

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.