The tremendous growth of biomedical text mining increases the publications in literature. The task of Information Extraction is to recognize the predefined set of concepts in a specific field. It will ignore unwanted irrelevant information’s. The recognizes the exact class of predefined entities, relationships and events. The manual identification of entity and relationships biomedical literature consumes much time and lengthy and laborious task. Automation of entity and relationship extraction addresses these issues. Various approaches are proposed to extract relationship from biomedical literature. This study analyses the range of approaches to automatic extraction of relationships from biomedical literature. The proposed algorithm for relation extraction from biomedical literature is based on Co-occurrence approaches. The system evaluated with three datasets such as Breast cancer corpus, Lung Cance Corpus and Thyroid Cancer corpus. And identified seven classes of entities and their respective relationships. The system evaluated using the Precision, Recall and F-measure. The performance of the system is compared with the Rule based approach. The proposed system out performs the existing system.
Volume 11 | 06-Special Issue
Pages: 1431-1437