Working Paper: NBER ID: w30603
Authors: Florenta Teodoridis; Jino Lu; Jeffrey L. Furman
Abstract: Understanding factors affecting the direction of innovation is a central aim of research in the economics of innovation. Progress on this topic has been inhibited by difficulties in measuring distance and movement in knowledge space. We describe a methodology that infers the mapping of the knowledge landscape based on text documents. The approach is based on an unassisted machine learning technique, Hierarchical Dirichlet Process (HDP), which flexibly identifies patterns in text corpora. The resulting mapping of the knowledge landscape enables calculations of distance and movement, measures that are valuable in several contexts for research in innovation. We benchmark and demonstrate the benefits of this approach in the context of 44 years of USPTO data.
Keywords: Innovation; Knowledge Space; Machine Learning; Natural Language Processing
JEL Codes: C55; C80; O3; O31; O32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Hierarchical Dirichlet Processes (HDP) (C11) | effective mapping of the knowledge landscape (O36) |
| effective mapping of the knowledge landscape (O36) | calculation of distance and movement measures (C49) |
| calculation of distance and movement measures (C49) | insights into the direction of innovation (O36) |
| insights into the direction of innovation (O36) | impact on economic growth (F69) |
| insights into the direction of innovation (O36) | competitive advantage (L21) |