About BCC-NER

Tagging biomedical entities such as gene, protein, cell, cell-line etc is the first step and an important pre-requisite in biomedical literature mining. In this paper, we describe our hybrid named entity tagging approach namely BCC-NER. BCC-NER is deployed with three modules. The first module is for text processing which includes basic NLP pre- processing, feature extraction and feature selection. The second module is for training and model building with bidirectional conditional random fields (CRF) to parse the text in both directions (forward and backward) and integrate the backward and forward trained models using margin infused relaxed algorithm (MIRA). The third and final module is for post-processing to achieve a better performance, which includes surrounding text features, parenthesis mismatching and two-tier abbreviation algorithm. The Evaluation results on BioCreative II GM test corpus of BCC-NER achieve a precision of 89.95, recall of 84.15 and overall F-score of 86.95, which is higher than the other currently available open source taggers.

Mr.Gurusamy Murugesan Ph.D Scholar
Ms.Sabenabau Abdulkhadar Ph.D Scholar
Mr.Balu Bhasuran Ph.D Scholar
Dr.Jeyakumar Natarajan Professor