关键词: 信息抽取, 半监督学习, Tri-training, 集成学习

Abstract: With the rapid growth of biomedical literature, the knowledge about diseases, symptoms and therapeutic substances in biomedical literature has contributed positively to drug discovery and disease therapy. This paper presents the method of constructing two models for extracting the relations between diseases and therapeutic substances, i.e. the disease and symptom model and symptom and therapeutic substance model. The disease and symptom model judges whether a disease can exist or cause a physiological phenomenon. The symptom and therapeutic substance model determines whether a substance changes a person’s physiological processes. In this method, a semi-supervised learning algorithm, Tri-training is applied to utilize the unlabeled data along with a few labeled examples to improve the classification performance. Experimental results show that exploiting unlabeled data with the Tri-training algorithms can enhance the experimental result. In the Tri-training process, this method uses ensemble learning to integrate three classifies, which can improve the learning performance.

Key words: unlabeled data, semi-supervised learning, Tri-training, ensemble learning