Patent Text and Long-Run Innovation Dynamics: The Critical Role of Model Selection / Ina Ganguli, Jeffrey Lin, Vitaly Meursault, Nicholas F. Reynolds.

Format:

Book

Author/Creator:

Ganguli, Ina.

Contributor:

Lin, Jeffrey.

Meursault, Vitaly.

Reynolds, Nicholas F.

National Bureau of Economic Research.

Series:

Working Paper Series (National Bureau of Economic Research) no. w32934.

NBER working paper series no. w32934

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2024.

Summary:

As distorted maps may mislead, Natural Language Processing (NLP) models may misrepresent. How do we know which NLP model to trust? We provide comprehensive guidance for selecting and applying NLP representations of patent text. We develop novel validation tasks to evaluate several leading NLP models. These tasks assess how well candidate models align with both expert and non-expert judgments of patent similarity. State-of-the-art language models significantly outperform traditional approaches such as TF-IDF. Using our validated representations, we measure a secular decline in contemporaneous patent similarity: inventors are "spreading out" over an expanding knowledge frontier. This finding is corroborated by declining rates of multiple invention from newly-digitized historical patent interference records. In contrast, selecting another single representation without validating alternatives yields an ambiguous or even opposing trend. Thus, our framework addresses a fundamental challenge of selecting among different black-box NLP models that produce varying economic measurements. To facilitate future research, we plan to provide our validation task data and embeddings for all US patents from 1836-2023.

Notes:

September 2024.

Print version record