现代情报2025，Vol.45Issue(10)：16-25,10.DOI:10.3969/j.issn.1008-0821.2025.10.002
基于语义关系和实体识别的潜在标准必要专利识别研究
Research on Potential Standard Essential Patent Recognition Based on Semantic Relationships and Entity Recognition
摘要
Abstract
[Purpose/Significance]This study addresses the challenges of entity recognition and long-distance depen-dencies in sequence modeling for potential Standard Essential Patent(SEP)identification tasks.The goal is to improve rec-ognition accuracy and enhance the interpretability of results,based on which,a novel model,XLNet-BiLSTM-CRF-CNN(XLBLCC)is proposed to identify potential SEPs.[Method/Process]The XLNet model was used to capture contextual semantics in patent text,providing rich vector representations and semantic relations.The BiLSTM-CRF model was applied to generate Named Entity Recognition(NER)tags,which helped identify the boundaries of entities in the text.To further enhance feature extraction,a CNN model was employed to learn the important characteristics of SEP text for accu-rate prediction.The model's performance was validated on a dataset containing SEPs from the ETSI database and non-SEPs from the Incopat database.[Result/Conclusion]The XLBLCC model outperform baseline models,achieving an accu-racy of 86%,an F1 score of 89%,and an AUC of 84%.The XLNet model demonstrate superior global semantic under-standing compared to models like BERT.In experiments comparing high-value patents with SEPs,the proposed model show strong generalization capabilities,making it an effective and robust tool for SEP identification in patent analysis.关键词
语义关系/潜在标准必要专利/XLNet/BiLSTM-CRF/CNNKey words
semantic relationships/potential standard essential patent/XLNet/BiLSTM-CRF/CNN分类
社会科学引用本文复制引用
窦路遥,周志刚,冯宇..基于语义关系和实体识别的潜在标准必要专利识别研究[J].现代情报,2025,45(10):16-25,10.基金项目
国家自然科学基金项目"多源数据融合场景下的对抗式隐私洞察靶向保护技术研究"(项目编号:61902226) （项目编号:61902226）
山西省自科面上项目"面向多边异构与连续任务驱动的联邦持续学习关键技术研究"(项目编号:202403021221217). （项目编号:202403021221217）
