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Natural Language Processing and Innovation Research

Antonin Bergeaud et al.

Annual Review of Economics2026https://doi.org/10.1146/annurev-economics-072225-113623article
AJG 3ABDC A*
Weight
0.50

Abstract

Innovation is central to models in economics, strategy, management, and finance, yet it remains difficult to measure due to its intangible and knowledge-based nature. Recent advancements in natural language processing offer new methods to analyze textual artifacts, providing empirical insights into previously hard-to-measure aspects of innovation. This article provides an overview of the current applications of these methods in empirical innovation research, highlights their transformative potential for reshaping how researchers study and quantify innovation, and discusses the critical challenges that accompany their use.

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https://doi.org/https://doi.org/10.1146/annurev-economics-072225-113623

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@article{antonin2026,
  title        = {{Natural Language Processing and Innovation Research}},
  author       = {Antonin Bergeaud et al.},
  journal      = {Annual Review of Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1146/annurev-economics-072225-113623},
}

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Natural Language Processing and Innovation Research

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.