Machine learning and natural language processing on the patent corpus: Data, tools, and new measures

Author:

Balsmeier Benjamin1,Assaf Mohamad23,Chesebro Tyler4,Fierro Gabe4,Johnson Kevin4,Johnson Scott4,Li Guan‐Cheng2,Lück Sonja5,O'Reagan Doug2,Yeh Bill4,Zang Guangzheng4,Fleming Lee2ORCID

Affiliation:

1. Centre for Research in Economics and Management University of Luxembourg Esch‐sur‐Alzette Luxembourg

2. Coleman Fung Institute for Engineering Leadership UC Berkeley Berkeley CA USA

3. Department of Electrical and Computer Engineering American University of Beirut Beirut Lebanon

4. UC Berkeley Electrical Engineering and Computer Science Berkeley CA USA

5. Department of Economics University of Paderborn Paderborn Germany

Funder

National Science Foundation

Publisher

Wiley

Subject

Management of Technology and Innovation,Strategy and Management,Economics and Econometrics,General Business, Management and Accounting,General Medicine

Reference14 articles.

1. Independent boards and innovation

2. Does Going Public Affect Innovation?

3. Carayol N. &Cassi L.(2009).Who's who in patents. A Bayesian approach. Cahiers du GREThA 2009–07.

4. Fleming L. &Juda A.(2004).A network of invention. USPTO Economics Working Paper No. 2013–2.

5. Hall B. H. &Harhoff D.(2012).Recent research on the economics of patents. NBER Working Paper 17773.

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