A Multilevel NER Framework for Automatic Clinical Name Entity Recognition

Abstract

In this paper, we propose a novel multilevel NER framework, for addressing the challenges of clinical name entity recognition, based on different machine learning and text mining algorithms. The proposed framework, with multiple levels, allows models for increasingly complex NER tasks to be built. The experimental evaluation on two different publicly available datasets, corresponding to different application contexts - the CLEF 2016 challenge shared task 1A for nursing handover context, and the BIONLP/NLPBPA 2004 challenge shared task on GENIA corpus for recognizing entities in microbiology, has validated the proposed framework.