TWO LEVEL SELF -SUPERVISED RELATION EXTRACTION FROM MEDLINE USING UMLS

The biomedical research literature is one among many other domains that hides a precious knowledge, and the biomedical community made an extensive use of this scientific literature to discover the facts of biomedical entities, such as disease, drugs,etc.MEDLINE is a huge database of biomedical research papers which remain a significantly underutilized source of biological information. Discovering the useful knowledge from such huge corpus leads to various problems related to the type of information such as the concepts related to the domain of texts and the semantic relationship associated with them. In this paper, we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified Medical Language System (UMLS) Knowledge base. The model uses a Self-supervised Approach for Relation Extraction (RE) by constructing enhanced training examples using information from UMLS. The model shows a better result in comparison with current state of the art and naive approaches.