Product functional information based automatic patent classification: Method and experimental studies
作者：
Highlights：
• This paper studied the automatic mining and classification of the functional information from the patent texts to support the product innovation design.
• The classification on the primary level functional groups has been carried out and the classifier with the best classification accuracy has been built through a couple of experiments.
• In overall, all three experiments obtained decent accurate classification results so it proved that the whole experiment design is an efficient attempt to extract the hidden patent information and classify them automatically.
• We can further study the experiment results and put forward some suggestions on the automatic patent text classification process.
摘要
•This paper studied the automatic mining and classification of the functional information from the patent texts to support the product innovation design.•The classification on the primary level functional groups has been carried out and the classifier with the best classification accuracy has been built through a couple of experiments.•In overall, all three experiments obtained decent accurate classification results so it proved that the whole experiment design is an efficient attempt to extract the hidden patent information and classify them automatically.•We can further study the experiment results and put forward some suggestions on the automatic patent text classification process.
论文关键词：Innovation design,Functional basis,Patent text classification,Naive Bayes,EM algorithm
论文评审过程：Received 19 April 2016, Revised 25 January 2017, Accepted 27 March 2017, Available online 29 March 2017, Version of Record 31 March 2017.
论文官网地址：https://doi.org/10.1016/j.is.2017.03.007
