iSimp: A sentence simplification system for biomedicail text

Text mining applications using natural language processing are often confronted with long and complicated sentences. This is observed particularly in the abstracts of scientific articles where authors summarize, in few sentences, the various facts described throughout the manuscript. Being rich in novel and important information, the abstract has been the primary target of biomedicai text mining applications. In this work, we aim to simplify complex sentences in abstracts of biomedicai text so that they can be readily processed by text mining applications. We focus on syntactic constructs that are frequently encountered in the biomedicai literature, such as coordinations, relative clauses, and appositions, with emphasis on their boundary detection. Our approach yielded good detection performance (average F-measure between 86.5% and 92.7%), and aided in improving biomedicai text mining applications, RLIMS-P and Rank_Pref.