Title :
A Genetic Approach for Biomedical Named Entity Recognition
Author :
Ekbal, Asif ; Saha, Sriparna ; Sikdar, Utpal Kumar ; Hasanuzzaman, Md
Author_Institution :
Univ. of Trento, Trento, Italy
Abstract :
In this paper, we report a classifier ensemble technique using the search capability of genetic algorithm (GA) for Named Entity Recognition (NER) in biomedical domain. We use Maximum Entropy (ME) framework to build a number of classifiers depending upon the various representations of a set of features. The proposed technique is evaluated with the JNLPBA 2004 data sets that yield the overall recall, precision and F-measure values of 67.98%, 71.68% and 69.78%, respectively.
Keywords :
biology computing; genetic algorithms; maximum entropy methods; pattern classification; F-measure value; biomedical domain; biomedical named entity recognition; classifier ensemble; data set; genetic algorithm; maximum entropy framework; search capability; Biological cells; Context; Entropy; Gallium; Genetic algorithms; Genetics; Training data; Biomedical named entity recognition; Genetic algorithm; Maximum Entropy;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on
Conference_Location :
Arras
Print_ISBN :
978-1-4244-8817-9
DOI :
10.1109/ICTAI.2010.125
