基于朴素贝叶斯模型的中文关键词提取算法研究

提出了一种基于朴素贝叶斯模型的中文关键词提取算法。该算法首先通过训练过程获得朴素贝叶斯模型中的各个参数，然后以之为基础，在测试过程完成关键词提取。实验表明，相对于传统的if*idf方法，该算法可从小规模的文档集中提取出更为准确的关键词，而且可灵活地增加表征词语重要性的特征项，因而具有更好的可扩展性。

Study on Chinese keyword extraction algorithm based on Na(l)ve Bayes model

A keyword extraction algorithm for Chinese documents based on Na ve Bayes model was proposed,which involved training and testing process.Parameters of the model were first obtained during training process,and then the probability of a word to be a keyword was computed based on the model during testing process.Experiment results show that the algorithm can extract more accurate keywords from a small scale document collection compared with traditional approach of if*idf.Moreover,it can flexibly extend feature items that indicate the importance of words,so it has a good scalability.