关键词: 文本分类, 支持向量机, 简单向量距离分类, 命名实体识别

Abstract: Abstract: Automation text classification can greatly help people to analyze a mass of biomedical literature. The results of TREC2005 genomics track showed that Support Vector Machine has obvious advantages over other models. The paper compares the performance of classification based on distance of simple vectors with those based on SVM on the TREC data sets. The results show that classification based on distance of simple vectors are not worse than those based on SVM in this domain and the pre-process via a named entity recognition can improve the performance.

Key words: Automatic Text Classification, Support Vector Machine, Simply Vector Distance Clustering, Named Entity Recognition