南京师大学报（自然科学版）Issue(1)：57-60,65,5.
基于改进权重的贝叶斯推理和TFIDF算法文本主题词提取研究
Research of Text Subject Extraction Based on Improved Weight for Bayesian Reasoning and TFIDF Algorithm
摘要
Abstract
The shortcoming of the TFIDF algorithm is improved for Chinese text topic word extraction. This paper considers the keywords appearing frequency,position weight in the text,the hybrid algorithm of Bayesian Reasoning and TFIDF was designed to extracte topic words,and the topic words was extracted from forward,reverse and middle based on sorting position of the candidate words. The results was higher average accuracy than the simple TFIDF by 6. 2%.关键词
贝叶斯推理/位置权重/提取/TFIDF算法Key words
Bayesian reasoning/position weight/topic words extraction/TFIDF algorithm分类
信息技术与安全科学引用本文复制引用
邵晓根,鞠训光,胡局新,马忠伟..基于改进权重的贝叶斯推理和TFIDF算法文本主题词提取研究[J].南京师大学报（自然科学版）,2014,(1):57-60,65,5.基金项目
科技部国家中小企业创新基金项目(11C26213204533)、徐州市科技计划项目(XF11C052) （11C26213204533）
