Title: Co-decision matrix framework for name entity recognition in biomedical text
Authors: Haochang Wang; Yu Li
Addresses: School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China; College of Computer and Information Technology, Northeast Petroleum University, Daqing, Heilongjiang, 163318, China ' School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
Abstract: As a new branch of data mining and knowledge discovery, the research of biomedical text mining has a rapid progress currently. Biomedical named entity (BNE) recognition is a basic technique in the biomedical knowledge discovery and its performance has direct effects on further discovery and processing in biomedical texts. In this paper, we present an improved method based on co-decision matrix framework for Biomedical Named Entity Recognition (BNER). The relativity between classifiers is utilised by using co-decision matrix to exchange decision information among classifiers. The experiments are carried on GENIA corpus with the best result of 75.9% F-score. Experimental results show that the proposed method, co-decision matrix framework, can yield promising performances.
Keywords: name entity recognition; co-decision matrix; generalised winnow; feature selection; biomedical texts; bioinformatics; data mining; knowledge discovery; text mining; biomedical information retrieval.
DOI: 10.1504/IJDMB.2015.067956
International Journal of Data Mining and Bioinformatics, 2015 Vol.11 No.4, pp.412 - 423
Received: 31 Dec 2012
Accepted: 20 Apr 2013
Published online: 12 Mar 2015 *