With the growing impact of ESG on businesses, research related to renewable energy is receiving great attention. Solar cells are one of them, and accordingly, it can be said that the research value of solar cell patent analysis is very high. Patent documents have high research value. Being able to accurately analyze and classify patent documents can reveal several important technical relationships. It can also describe the business trends in that technology. And when it comes to investment, new industrial solutions will also be inspired and proposed to make important decisions. Therefore, we must carefully analyze patent documents and utilize the value of patents. To solve the solar cell patent classification problem, we propose a keyword extraction method and a deep neural network-based solar cell patent classification method. First, solar cell patents are analyzed for pretreatment. It then uses the KeyBERT algorithm to extract keywords and key phrases from the patent abstract to construct a lexical dictionary. We then build a solar cell patent classification model according to the deep neural network. Finally, we use a deep neural network-based solar cell patent classification model to classify power patents, and the training accuracy is greater than 95%. Also, the validation accuracy is about 87.5%. It can be seen that the deep neural network method can not only realize the classification of complex and difficult solar cell patents, but also have a good classification effect.
Query Generation for Patent Retrieval with Keyword Extraction based on Syntactic Features
Amsterdam Business School, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹商学院）；European Patent Office（欧洲专利局）；Institute of Informatics, University of Amsterdam（阿姆斯特丹大学信息学院）
BioChemInsight: An Online Platform for Automated Extraction of Chemical Structures and Activity Data from Patents
Zhejiang University（浙江大学）；Huadong Medicine Co., Ltd.（华东医药股份有限公司）；Hangzhou VicrobX Biotech Co., Ltd.（杭州维康信生物科技有限公司）；Shanghai University（上海大学）；ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙江大学杭州国际科创中心）；Jiangxi University of Chinese Medicine（江西中医药大学）；Second Military Medical University（第二军医大学）
RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation
Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Usher Institute, Medical School, University of Edinburgh（爱丁堡大学医学院厄舍研究所）
Building a Multimodal Dataset of Academic Paper for Keyword Extraction
Department of Information Management, Nanjing University of Science and Technology（南京理工大学经济管理学院信息管理系）；Department of Archives and E-government, Soochow University（苏州大学社会学院档案与电子政务系）
Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness
Institute of Cyber Security for Society (iCSS) & School of Computing（社会网络安全研究所（iCSS）及计算机学院）；University of Kent（肯特大学）；Shanghai Jiao Tong University（上海交通大学）
Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models
Department of Computing and Systems Engineering, Universidad Católica del Norte, Antofagasta, Chile（智利天主教大学北部校区计算与系统工程系，安托法加斯塔，智利）；Department of Industrial Engineering, Universidad Católica del Norte, Antofagasta, Chile（智利天主教大学北部校区工业工程系，安托法加斯塔，智利）；Department of Chemical and Environmental Engineering, Universidad Católica del Norte, Antofagasta, Chile（智利天主教大学北部校区化学与环境工程系，安托法加斯塔，智利）
LAME: Layout Aware Metadata Extraction Approach for Research Articles
Kumoh National Institute of Technology（国立金乌工科大学）；Korea Institute of Science and Technology Information（韩国科学技术信息研究院）；Korea University of Technology and Education（韩国技术教育大学）
