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Abstract The Institutional Grammar (IG) is a rigorous tool for analyzing the laws and policies governing nonprofit organizations; however, its use was limited due to the time-consuming nature of hand-coding. We introduce an advance in Natural Language Processing using a semantic role labeling (SRL) classifier that reliably codes rules governing and guiding nonprofit organizations. This paper provides guidance for how to hand-code using the IG, preprocess text for machine learning, and demonstrates the SRL classifier for automated IG coding. We then compare the hand-coding to the SRL coding to demonstrate its accuracy. The advances in machine learning now make it feasible to utilize the IG for nonprofit research questions focused on inter-organizational collaborations, government contracts, federated nonprofit organizational compliance, and nonprofit governance, among others. An added benefit is that the IG is adaptable for different languages, thus enabling cross-national comparative research. By providing examples throughout the paper, we demonstrate how to use the IG and the SRL classifier to address research questions of interest to nonprofit scholars.