基于贝叶斯推理和TFIDF算法的中文关键词智能抽取  

Based on the Bayesian Reasoning and TFIDF Algorithm of Chinese KeyWords Intelligent Extraction

作　　者：胡局新[1] 鞠训光[1] 

机构地区：[1]徐州工程学院信电工程学院,江苏徐州221008

出　　处：《微电子学与计算机》2012年第9期197-200,共4页Microelectronics & Computer

基　　金：徐州市科技项目(XF11CO52)

摘　　要：针对传统的TFIDF中文关键词智能抽取模型中,遇到冗余、或者动态性较强的词汇时,词汇挖掘效果不好的问题,提出一种基于贝叶斯推理和TFIDF算法的中文关键词智能抽取方法.利用贝叶斯统计原理对文本信息进行概率化的统计,运用贝叶斯决策理论对TFIDF算法进行优化,克服传统的TFIDF算法存在着缺陷.实验结果表明,优化后的TFIDF算法在进行中文关键词智能抽取中,抽取的准确性大幅提高.In traditional TFIDF Chinese keywords intelligent extraction model, meet the redundant, or dynamic words, the words mining result is bad. Proposed based on the bayesian reasoning and TFIDF algorithm of Chinese keywords intelligent extraction method. Using bayesian statistical principle to text information of probability statistics. Using bayesian decision-making theory to optimize TFIDF algorithm. Overcome traditional TFIDF algorithm of defects. The experimental results show that the optimal algorithm for Chinese keywords TFIDF in intelligent extraction, the accuracy of the extraction greatly increased.

关 键 词：TFIDF 文本挖掘 贝叶斯统计 关键词提取 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]