Study on Automatic Classification of Patents Oriented to TRIZ
Hu Zhengyin1,2, Fang Shu1, Wen Yi1, Zhang Xian1,2, Liang Tian1
1. Chengdu Document and Information Center, Chinese Academy of Sciences, Chengdu 610041, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China
[Objective] This paper proposes an approach to automatically classify patents oriented to TRIZ applications based on a personalized classification system. [Methods] A personalized classification system is constructed in micro-macro-meso levels using topic model. Then, an appropriate feature and classifier are chosen to preliminarily classify patents. The classifier is optimized by smoothing unbalance data and reducing features dimensions. [Results] This approach implements semi-automatically constructing a personalized classification and automatically classifying patents oriented to TRIZ applications. In medium data size, this approach can classify patents with F-measure value of 90.2%. [Limitations] This approach is not available in small size data set and not verified in big size data set. [Conclusions] This paper can classify patents oriented to TRIZ applications in medium data size.
胡正银, 方曙, 文奕, 张娴, 梁田. 面向TRIZ的专利自动分类研究[J]. 现代图书情报技术, 2015, 31(1): 66-74.
Hu Zhengyin, Fang Shu, Wen Yi, Zhang Xian, Liang Tian. Study on Automatic Classification of Patents Oriented to TRIZ. New Technology of Library and Information Service, 2015, 31(1): 66-74.
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