Machine learning and natural language processing on the patent corpus: Data, tools, and new measures
B. Balsmeier, M. Assaf, T. Chesebro, G. Fierro, K. Johnson, S. Johnson, G. Li, S. Lück, D. O’Reagan, B. Yeh, G. Zang, L. Fleming, Journal of Economics & Management Strategy 27 (2018) 535–553.
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Journal Article
| Published
| English
Author
Balsmeier, Benjamin;
Assaf, Mohamad;
Chesebro, Tyler;
Fierro, Gabe;
Johnson, Kevin;
Johnson, Scott;
Li, Guan‐Cheng;
Lück, SonjaLibreCat 
;
O'Reagan, Doug;
Yeh, Bill;
Zang, Guangzheng;
Fleming, Lee
All
;
O'Reagan, Doug;
Yeh, Bill;
Zang, Guangzheng;
Fleming, Lee
All
Department
Abstract
Drawing upon recent advances in machine learning and natural language processing, we introduce new tools that automatically ingest, parse, disambiguate, and build an updated database using U.S. patent data. The tools identify unique inventor, assignee, and location entities mentioned on each granted U.S. patent from 1976 to 2016. We describe data flow, algorithms, user interfaces, descriptive statistics, and a novelty measure based on the first appearance of a word in the patent corpus. We illustrate an automated coinventor network mapping tool and visualize trends in patenting over the last 40 years.
Keywords
Publishing Year
Journal Title
Journal of Economics & Management Strategy
Volume
27
Issue
3
Page
535-553
LibreCat-ID
Cite this
Balsmeier B, Assaf M, Chesebro T, et al. Machine learning and natural language processing on the patent corpus: Data, tools, and new measures. Journal of Economics & Management Strategy. 2018;27(3):535-553. doi:10.1111/jems.12259
Balsmeier, B., Assaf, M., Chesebro, T., Fierro, G., Johnson, K., Johnson, S., Li, G., Lück, S., O’Reagan, D., Yeh, B., Zang, G., & Fleming, L. (2018). Machine learning and natural language processing on the patent corpus: Data, tools, and new measures. Journal of Economics & Management Strategy, 27(3), 535–553. https://doi.org/10.1111/jems.12259
@article{Balsmeier_Assaf_Chesebro_Fierro_Johnson_Johnson_Li_Lück_O’Reagan_Yeh_et al._2018, title={Machine learning and natural language processing on the patent corpus: Data, tools, and new measures}, volume={27}, DOI={10.1111/jems.12259}, number={3}, journal={Journal of Economics & Management Strategy}, publisher={Wiley}, author={Balsmeier, Benjamin and Assaf, Mohamad and Chesebro, Tyler and Fierro, Gabe and Johnson, Kevin and Johnson, Scott and Li, Guan‐Cheng and Lück, Sonja and O’Reagan, Doug and Yeh, Bill and et al.}, year={2018}, pages={535–553} }
Balsmeier, Benjamin, Mohamad Assaf, Tyler Chesebro, Gabe Fierro, Kevin Johnson, Scott Johnson, Guan‐Cheng Li, et al. “Machine Learning and Natural Language Processing on the Patent Corpus: Data, Tools, and New Measures.” Journal of Economics & Management Strategy 27, no. 3 (2018): 535–53. https://doi.org/10.1111/jems.12259.
B. Balsmeier et al., “Machine learning and natural language processing on the patent corpus: Data, tools, and new measures,” Journal of Economics & Management Strategy, vol. 27, no. 3, pp. 535–553, 2018, doi: 10.1111/jems.12259.
Balsmeier, Benjamin, et al. “Machine Learning and Natural Language Processing on the Patent Corpus: Data, Tools, and New Measures.” Journal of Economics & Management Strategy, vol. 27, no. 3, Wiley, 2018, pp. 535–53, doi:10.1111/jems.12259.
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