基于功能基的专利信息挖掘与自动分类实验研究

Information Extraction Based on Functional Basis and Experimental Study on Automatic Classification

为了在产品创新设计过程为设计者提供跨领域的专利知识,提出一种以功能基为分类标准的中文专利文本分类方法。针对功能基类别多、专利文本训练集少的特点,从简化类别数量和增加数据集2个角度出发,采用多重二分类监督分类算法和基于EM算法的半监督分类算法,以朴素贝叶斯(NB)完全有监督算法为对照,采用正交实验,考察特征选择与数据集选择对分类准确度的影响,实现一级功能基分类准确率达到80%,基本符合应用要求。为基于功能基辅助产品创新设计专利知识库的构建,提供了相关的技术支持。

In order to provide interdisciplinary patent knowledge for designer in product innovative design process, a Chinese patent text classification method was proposed based on functional basis. Because functional basis has so many categories and the number of train- ing sets for patent text is less, multiple binary classification algorithm and EM semi-supervised learning algorithm were adopted to classi- fy Chinese patent text. Compared with the results of naive Bayesian （NB） fully supervised classification experimental, by using the or- thogonal experimental design, the selection of features and data sets were considered according to the accuracy of classification,the classifying accuracy rate of the first class reached above 80% which was in accordance with application requirements. This study provided core technologies for the construction of patent knowledge base based on functional basis in product innovative design system.