Enhancing Biomedical Named Entity Classification Using Terabyte Unlabeled Data

摘要：

This paper presents a semi-supervised learning method to enhance biomedical named entity classification using features generated from labeled and terabyte unlabeled data,called Feature Coupling Degree (FCD) features.Highly discriminative context words are obtained from labeled free text using Chi-square method and queries formed by combining the named entity and context words are retrieved by search engine.Then the retrieved web page counts are converted into binary features by discretization.We investigate the effect of this type of feature in a biomedical corpus generated from several online resources.Support Vector Machine (SVM) is used as classifier and the performances of different features with various kernels and discretization methods are compared.The results show that the method enhances the classification performance especially for Out-of-Vocabulary (OOV) terms and relative small size of training data.In addition,only using FCD features with polynomial kernels,the performance is competitive to classical features.

关键词： semi-supervised learning biomedical named entity classification discretization SVM polynomial kernel

作者: Yanpeng Li Hongfei Lin Zhihao Yang

作者单位: Department of Computer Science and Engineering,Dalian University of Technology,Dalian,China,116024

会议类型: 国际会议

会议名称: 4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)

会议地点: 哈尔滨

会议语种:英文

页码: 605-612

在线出版日期: 2008-01-16（万方平台首次上网日期，不代表论文的发表时间）