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NLP4Gov: A Comprehensive Library for Computational Policy Analysis

Mahasweta Chakraborti, Sailendra Akash Bonagiri, Santiago Virgüez-Ruiz, Seth Frey

TL;DR

The paper tackles the challenge of scaling formal policy analysis for online governance by introducing NLP4Gov, a modular, Colab-based toolkit that converts policy texts into semantic and symbolic representations. It combines Institutional Grammar 2.0 with dependency parsing and Semantic Role Labeling to extract ABDICO constituents, supported by end-to-end pipelines for coreference resolution, parsing, and clustering, as well as applications for policy comparison and exploration. Validation across multiple datasets demonstrates practical parsing performance, and the system supports visualization of institutional networks using the SNR taxonomy. This work enables reproducible, cross-platform, and scalable computational policy analysis, with potential to inform governance design and evaluation in digital communities and open-source ecosystems.

Abstract

Formal rules and policies are fundamental in formally specifying a social system: its operation, boundaries, processes, and even ontology. Recent scholarship has highlighted the role of formal policy in collective knowledge creation, game communities, the production of digital public goods, and national social media governance. Researchers have shown interest in how online communities convene tenable self-governance mechanisms to regulate member activities and distribute rights and privileges by designating responsibilities, roles, and hierarchies. We present NLP4Gov, an interactive kit to train and aid scholars and practitioners alike in computational policy analysis. The library explores and integrates methods and capabilities from computational linguistics and NLP to generate semantic and symbolic representations of community policies from text records. Versatile, documented, and accessible, NLP4Gov provides granular and comparative views into institutional structures and interactions, along with other information extraction capabilities for downstream analysis.

NLP4Gov: A Comprehensive Library for Computational Policy Analysis

TL;DR

The paper tackles the challenge of scaling formal policy analysis for online governance by introducing NLP4Gov, a modular, Colab-based toolkit that converts policy texts into semantic and symbolic representations. It combines Institutional Grammar 2.0 with dependency parsing and Semantic Role Labeling to extract ABDICO constituents, supported by end-to-end pipelines for coreference resolution, parsing, and clustering, as well as applications for policy comparison and exploration. Validation across multiple datasets demonstrates practical parsing performance, and the system supports visualization of institutional networks using the SNR taxonomy. This work enables reproducible, cross-platform, and scalable computational policy analysis, with potential to inform governance design and evaluation in digital communities and open-source ecosystems.

Abstract

Formal rules and policies are fundamental in formally specifying a social system: its operation, boundaries, processes, and even ontology. Recent scholarship has highlighted the role of formal policy in collective knowledge creation, game communities, the production of digital public goods, and national social media governance. Researchers have shown interest in how online communities convene tenable self-governance mechanisms to regulate member activities and distribute rights and privileges by designating responsibilities, roles, and hierarchies. We present NLP4Gov, an interactive kit to train and aid scholars and practitioners alike in computational policy analysis. The library explores and integrates methods and capabilities from computational linguistics and NLP to generate semantic and symbolic representations of community policies from text records. Versatile, documented, and accessible, NLP4Gov provides granular and comparative views into institutional structures and interactions, along with other information extraction capabilities for downstream analysis.
Paper Structure (11 sections, 1 figure, 4 tables)

This paper contains 11 sections, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Setup and Usage
  4. Applications and Pipelines
  5. Institutional Grammar Framework
  6. Parsing Policy Constituents
  7. Modeling and Visualizing Institutions
  8. Further Work
  9. Conclusion
  10. Acknowledgement
  11. Validation Data: Institutional Grammar

Figures (1)

  • Figure 1: Projects in the Apache Software Foundation Incubator often comprise volunteer developers and are mostly directed by strategies, with fewer strong regulations or restrictions. The network was generated by parsing policy constituents and aggregating similar actors/objects into nodes. Edges are directed from actors to objects and are logarithmically weighted by the number of policies between the pair. Node labels are the top representative words from each cluster. Projects are nodal actors and objects and are subject to certain recommended and binding practices towards their communities, software releases, the Incubator, Foundation board, Management Committees (PPMC/IPMC), and mentors. Notably, there are very few restrictions, and they are only applicable with regard to project releases.