Boosting Performance of Gene Mention Tagging System by Classifiers Ensemble

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.

关键词： Classifiers Ensemble Gene Mention Tagging Named Entity Recognition Text Mining

作者: Lishuang LI Jing SUN Degen HUANG

作者单位: School of Computer Science and Engineering,Dalian University of Technology Dalian, Liaoning, China School of Computer Science and Engineering, Dalian University of Technology Dalian, Liaoning, China

会议类型: 国际会议

会议名称: The 6th International Conference on Natural Language Processing and Knowledge Engineering(第六届IEEE自然语言处理与知识工程国际会议 NLP-KE 2010)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2010-08-21（万方平台首次上网日期，不代表论文的发表时间）