Boosting performance of gene mention tagging system by hybrid methods

Abstract

NER (Named Entity Recognition) in biomedical literature is presently one of the internationally concerned NLP (Natural Language Processing) research questions. In order to get higher performance, a hybrid experimental framework is presented for the gene mention tagging task. Six classifiers are firstly constructed by four toolkits (CRF++, YamCha, Maximum Entropy (ME) and MALLET) with different training methods and features sets, and then combined with three different hybrid methods respectively: simple set operation method, voting method and two layer stacking method. Experiments carried out on the corpus of BioCreative II GM task show that the three hybrid methods get the F-measure of 87.40%, 87.31% and 87.70% separately without any post-processing, which are all higher than those of any single ones. Our best hybrid method (two layer stacking method) achieves an F-measure of 88.42% after post-processing, which outperforms most of the state-of-the-art systems. We also discuss the influence on the performance of the ensemble system by the number, performance and divergence of single classifiers in each hybrid method, and give the corresponding analysis why our hybrid models can improve the performance.