Conference Proceedings
Self-supervised relation extraction using UMLS
R Roller, M Stevenson
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics | Published : 2014
Abstract
Self-supervised relation extraction uses a knowledge base to automatically annotate a training corpus which is then used to train a classifier. This approach has been successfully applied to different domains using a range of knowledge bases. This paper applies the approach to the biomedical domain using UMLS, a large biomedical knowledge base containing millions of concepts and relations among them. The approach is evaluated using two different techniques. The presented results are promising and indicate that UMLS is a useful resource for semi-supervised relation extraction. © 2014 Springer International Publishing.