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DeepPatent: patent classification with convolutional neural networks and word embedding

Citations

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Cited by:

  1. Bekamiri, Hamid & Hain, Daniel S. & Jurowetzki, Roman, 2024. "PatentSBERTa: A deep NLP based hybrid model for patent distance and classification using augmented SBERT," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
  2. Bernhard Ganglmair & Alexander Kann, 2025. "Looking for Innovation Beyond the Patent System: Evidence from Research Disclosures," CRC TR 224 Discussion Paper Series crctr224_2025_656, University of Bonn and University of Mannheim, Germany.
  3. Meindl, Benjamin & Ayala, Néstor Fabián & Mendonça, Joana & Frank, Alejandro G., 2021. "The four smarts of Industry 4.0: Evolution of ten years of research and future perspectives," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
  4. Tadeusz A. Grzeszczyk & Michal K. Grzeszczyk, 2021. "Improving the Discovery of Technological Opportunities Using Patent Classification Based on Explainable Neural Networks," European Research Studies Journal, European Research Studies Journal, vol. 0(3 - Part ), pages 402-409.
  5. Hamid Bekamiri & Daniel S. Hain & Roman Jurowetzki, 2021. "PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT," Papers 2103.11933, arXiv.org, revised Oct 2021.
  6. Danielle Lee, 2024. "Exploring the determinants of research performance for early-career researchers: a literature review," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(1), pages 181-235, January.
  7. Yuki Hoshino & Yoshimasa Utsumi & Yoshiro Matsuda & Yoshitoshi Tanaka & Kazuhide Nakata, 2023. "IPC prediction of patent documents using neural network with attention for hierarchical structure," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-17, March.
  8. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
  9. Ascione, Grazia Sveva, 2023. "Technological diversity to address complex challenges: the contribution of American universities to sdgs," MPRA Paper 119452, University Library of Munich, Germany.
  10. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
  11. Yonghe Lu & Lehua Chen & Xinyu Tong & Yongxin Peng & Hou Zhu, 2024. "Research on cross-lingual multi-label patent classification based on pre-trained model," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3067-3087, June.
  12. Occhini, Giulia & Tranos, Emmanouil & Wolf, Levi John, 2023. "Measuring a country’s digital industrial structure: commercial websites and weakly supervised classification to the rescue," SocArXiv h572n, Center for Open Science.
  13. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
  14. Arousha Haghighian Roudsari & Jafar Afshar & Wookey Lee & Suan Lee, 2022. "PatentNet: multi-label classification of patent documents using deep learning based language understanding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 207-231, January.
  15. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
  16. Jie Chen & Jialin Chen & Shu Zhao & Yanping Zhang & Jie Tang, 2020. "Exploiting word embedding for heterogeneous topic model towards patent recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2091-2108, December.
  17. Mark Bukowski & Sandra Geisler & Thomas Schmitz-Rode & Robert Farkas, 2020. "Feasibility of activity-based expert profiling using text mining of scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 579-620, May.
  18. Jaewoong Choi & Jiho Lee & Janghyeok Yoon & Sion Jang & Jaeyoung Kim & Sungchul Choi, 2022. "A two-stage deep learning-based system for patent citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6615-6636, November.
  19. Juite Wang, 0000. "Analyzing and Predicting R&D Collaboration Networks in the Metaverse Industry," Proceedings of Economics and Finance Conferences 14716418, International Institute of Social and Economic Sciences.
  20. repec:ers:journl:v:xxiv:y:2021:i:3:p:402-409 is not listed on IDEAS
  21. Anqi Ma & Yu Liu & Xiujuan Xu & Tao Dong, 2021. "A deep-learning based citation count prediction model with paper metadata semantic features," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6803-6823, August.
  22. Doina Caragea & Theodor Cojoianu & Mihai Dobri & Andreas Hoepner & Oana Peia & Davide Romelli, 2024. "Competition and Innovation in the Financial Sector: Evidence from the Rise of FinTech Start-ups," Journal of Financial Services Research, Springer;Western Finance Association, vol. 65(1), pages 103-140, February.
  23. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
  24. Liang Chen & Shuo Xu & Lijun Zhu & Jing Zhang & Xiaoping Lei & Guancan Yang, 2020. "A deep learning based method for extracting semantic information from patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 289-312, October.
  25. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
  26. Peng Shao & Runhua Tan & Qingjin Peng & Wendan Yang & Fang Liu, 2023. "An Integrated Method to Acquire Technological Evolution Potential to Stimulate Innovative Product Design," Mathematics, MDPI, vol. 11(3), pages 1-24, January.
  27. Ningzi Li & Shiyang Lai & James Evans, 2025. "Big Data and the Computational Social Science of Entrepreneurship and Innovation," Papers 2505.08706, arXiv.org.
  28. Liyuan Zhang & Wei Liu & Yufei Chen & Xiaodong Yue, 2022. "Reliable Multi-View Deep Patent Classification," Mathematics, MDPI, vol. 10(23), pages 1-13, December.
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