Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance Relation Extraction from Biomedical Literature.

With the rapid growth of biomedical literature, a large amount of knowledge about diseases, symptoms, and therapeutic substances hidden in the literature can be used for drug discovery and disease therapy. In this paper, we present a method of constructing two models for extracting the relations between the disease and symptom and symptom and therapeutic substance from biomedical texts, respectively. The former judges whether a disease causes a certain physiological phenomenon while the latter determines whether a substance relieves or eliminates a certain physiological phenomenon. These two kinds of relations can be further utilized to extract the relations between disease and therapeutic substance. In our method, first two training sets for extracting the relations between the disease-symptom and symptom-therapeutic substance are manually annotated and then two semisupervised learning algorithms, that is, Co-Training and Tri-Training, are applied to utilize the unlabeled data to boost the relation extraction performance. Experimental results show that exploiting the unlabeled data with both Co-Training and Tri-Training algorithms can enhance the performance effectively.