[Purpose/significance] In order to achieve rapid classification in a large number of Chinese patents to meet the requirements of patent examination and intelligence analysis.[Method/process] Combined with the inherent format of patent text and the fact that there are multiple classification numbers, this paper applied multi-instance multi-label learning to automatic patent classification. Firstly, several classical multi-instance multi-label learning methods were introduced, and then these methods were applied to determine IPC number of Chinese patent.[Result/conclusion] It is experimentally demonstrated that the multi-instance multi-label learning methods are suitable for patent automatic classification, according to average precision, hamming loss, ranking loss, one error, coverage, training time, it is found that MIMLRBF can be used to determine the IPC number of Chinese patents quickly and accurately, which provides a new perspective for classifying large-scale patents.
Bao Xiang
,
Liu Guifeng
,
Cui Jinghua
. Application of Multi Instance Multi Label Learning in Chinese Patent Automatic Classification[J]. Library and Information Service, 2021
, 65(8)
: 107
-113
.
DOI: 10.13266/j.issn.0252-3116.2021.08.011
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