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Patent document clustering with deep embeddings

Abstract

The analysis of scientific and technical documents is crucial in the process of establishing science and technology strategies. One popular method for such analysis is for field experts to manually classify each scientific or technical document into one of several predefined technical categories. However, not only is manual classification error-prone and expensive, but it also requires extended efforts to handle frequent data updates. In contrast, machine learning and text mining techniques enable cheaper and faster operations, and can alleviate the burden on human resources. In this paper, we propose a method for extracting embedded feature vectors by applying a neural embedding approach for text features in patent documents and automatically clustering the embedding features by utilizing a deep embedding clustering method.

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Notes

  1. http://www.kipris.or.kr/khome/main.jsp.

  2. http://biz.kista.re.kr/patentmap.

  3. https://github.com/TeamLab/pdcde2018.

  4. http://scikit-learn.org/0.19/datasets/twenty_newsgroups.html.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant and funded by the Korean government (No. NRF-2015R1C1A1A01056185 and 2018R1D1A1B07045825).

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Correspondence to Eunjeong Park or Sungchul Choi.

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Eunjeong Park and Sungchul Choi are co-corresponding authors.

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Kim, J., Yoon, J., Park, E. et al. Patent document clustering with deep embeddings. Scientometrics 123, 563–577 (2020). https://doi.org/10.1007/s11192-020-03396-7

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  • DOI: https://doi.org/10.1007/s11192-020-03396-7

Keywords

  • Information embedding
  • Patent clustering
  • Deep learning
  • Text mining

Mathematics Subject Classification

  • 68U15