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NLP for Government

Government institutions increasingly recognize the potential of natural language processing (NLP) to transform the way policies are designed, evaluated, and implemented. From large-scale data analysis to supporting decision-making, NLP provides tools for extracting structured insights from unstructured information. However, deploying such methods in governmental contexts requires careful consideration of both opportunities and challenges.
This project explores how NLP can enable more effective governance by providing scalable solutions for analyzing large and complex datasets. Frameworks are developed to map the challenges of applying NLP to the public sector, including issues of data quality, bias, and transparency. Ethical and privacy considerations are emphasized to ensure responsible use of these technologies.
Through this research, we aim to demonstrate the potential of NLP to support evidence-based policy-making, improve monitoring and evaluation, and contribute to more efficient and inclusive governance processes. By addressing both technical and ethical dimensions, the project highlights how NLP can serve as a catalyst for innovation in government.

Objectives:

• Explore how natural language processing can support data-driven governance and policy-making.
• Investigate large-scale data analysis techniques to extract actionable insights from text and related sources.
• Develop frameworks to identify challenges and opportunities in applying NLP to public-sector decision-making.
• Examine ethical, privacy, and transparency considerations when deploying NLP in governmental contexts.
• Propose scalable methods to integrate NLP systems into workflows that improve planning, monitoring, and service delivery.

Keywords: Big Data | Natural Language Processing | Machine Learning / Deep Learning | Sinhala |

Publications

Team

External Collaborators: | Sriganesh Lokanathan | Gabriel E Kreindler | Yuhei Miyauchi | Yudhanjaya Wijeratne |