A Study on Relationship Extraction from Text Data
The tremendous growth of Biomedical text mining increases the publications in literature. The task of Information Extraction is to identify the predefined set of concepts in a specific field. It will disregard unwanted irrelevant information’s. And recognizes the specific 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 this issues. Various approaches are proposed to extract relationship from biomedical literature. This study analyses a range of approaches to automatic extraction of relationships from biomedical literature. It investigates various methods of relation Extraction System based on the working approach of the systems. The study includes the relation extraction approaches like Co-occurrence based approach, pattern based approach, Rule Based approach and machine Learning Based approaches. The outcomes of the systems are compared using the gold standards of text mining such as precision, recall and F-Measure.
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AbdulWahab Muzaffar, Farooque Azam, and Usman Qamar, A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set, National University of Sciences and Technology (NUST), H-12, Islamabad 44000, Pakistan Correspondence should be addressed to AbdulWahab Muzaffar; email@example.com Received 5 May 2015; Revised 17 June 2015; Accepted 29 June 2015
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