GENOME: A New Geopolitical Event Methodology and Dataset using Large Language Models
Summary
GENOME (Geopolitical Event News Observatory, Mapping, and Extraction) is a new automatically extracted dataset designed to enhance quantitative International Relations research. It addresses the limitations of existing automated datasets, which often lack comprehensive and up-to-date coverage of both conflictual and cooperative geopolitical interactions. GENOME implements PLOVER's 16 event types and expands the traditional Actor–Recipient schema by incorporating a Third Party role, enabling the capture of complex multilateral relations from newswire data. Its processing pipeline utilizes GPT models with one-shot prompting and enforced structured outputs for event extraction, ontology-based classification, entity normalization, and deduplication. A five-month comparative analysis against the POLECAT dataset revealed that GENOME provides a more balanced distribution of cooperative events, including verbal interactions largely absent in POLECAT, and offers superior temporal precision by attributing events to their inferred date of occurrence rather than publication date, alongside effective deduplication.
Key takeaway
For International Relations researchers or data scientists building geopolitical event datasets, GENOME offers a robust methodology to overcome limitations in existing automated data. You should consider adopting its extended Actor–Recipient schema with a Third Party role to capture multilateral interactions more comprehensively. Furthermore, prioritize inferring event dates over publication dates for improved temporal precision in your analyses, enhancing the accuracy of your quantitative research.
Key insights
GENOME provides a new dataset and methodology for extracting balanced geopolitical event data, including cooperative interactions, using LLMs.
Principles
- Extend event schemas to capture multilateral relations.
- Prioritize inferred event dates over publication dates for precision.
- Large Language Models can automate complex event extraction.
Method
GENOME's pipeline performs event extraction, ontology-based classification, entity normalization, and deduplication, employing GPT models with one-shot prompting and enforced structured outputs from newswire data.
In practice
- Use GPT models for structured event extraction from text.
- Implement a Third Party role in event schemas for multilateral analysis.
- Deduplicate highly covered events to improve data quality.
Topics
- Geopolitical Event Data
- Large Language Models
- Event Extraction
- International Relations
- Dataset Methodology
- GPT Models
Best for: AI Scientist, Research Scientist, NLP Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.