Research on Chinese Patent Text Classification Model Based on BERT-Graphorme
[Purpose/significance]With the increasing number of patent applications and the complexity of patent classification categories,the paper constructs a Chinese patent text classification model based on BERT-Graphormer in order to improve the efficiency of patent classification,review quality and save human resources.[Method/process]The paper models the structure and information of patent labels by fine-tuning the Graphormer model,and enhances the text representation of BERT model by using the modeled label in-formation.[Result/conclusion]Compared with other baseline models,the Micro-F1 and Macro-F1 scores of the proposed model are increased by 1.6％and 3.5％.The study successfully achieved the automatic classification of multi-label patents.The model's classifi-cation effectiveness is significantly improved by modeling and integrating information from labels and text.
万方数据
维普
