基于贝叶斯模型的专利分类

Patent categorization based on Bayes model

朴素贝叶斯分类器理论基础好,分类精度高。利用特征词权重函数修改朴素贝叶斯分类器,进而利用它实现专利文本的自动分类,不仅减少了专利人工分类的工作量和分类错误,而且为技术跟踪、竞争分析等提供了有效支持。实验与应用表明改进的朴素贝叶斯分类器用来解决专利分类是有效的。

Based on naive bayes classifier having solid theory foundation and high accuracy rate of classification, the classical naive bayes classifier was firstly improved by using term weight function in text, and then the patent categorization was implemented. This approach not only reduced manual labor and the categorization error, but also supported for the technology tracing, competition intelligence etc.The experiments and applications illustrate that the improved naive bayes classifier can be utilized to classify patents efficiently.