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论文类型：会议论文

发表时间：2010-01-01

收录刊物：EI、Scopus

摘要：To further improve the tagging performance of single classifiers, a classifiers ensemble experimental framework is presented for gene mention tagging. In the framework, six classifiers are constructed by four toolkits (CRF++, YamCha, Maximum Entropy (ME) and MALLET) with different training methods and feature sets and then combined with a two-layer stacking algorithm. The recognition results of different classifiers are regarded as input feature vectors to be incorporated, and then a high-powered model is obtained. Experiments carried out on the corpus of BioCreative II GM task show that the classifiers ensemble method is effective and our best combination method achieves an F-score of 88.09%, which outperforms most of the top-ranked Bio-NER systems in the BioCreAtIvE II GM challenge. ?2010 IEEE.