2024 EACL EACL 2024

Timeline Extraction from Decision Letters Using ChatGPT

Abstract

AbstractFreedom of Information Act (FOIA) legislation grants citizens the right to request information from various levels of the government, and aims to promote the transparency of governmental agencies. However, the processing of these requests is often met with delays, due to the inherent complexity of gathering the required documents. To obtain accurate estimates of the processing times of requests, and to identify bottlenecks in the process, this research proposes a pipeline to automatically extract these timelines from decision letters of Dutch FOIA requests. These decision letters are responses to requests, and contain an overview of the process, including when the request was received, and possible communication between the requester and the relevant agency. The proposed pipeline can extract dates with an accuracy of .94, extract event phrases with a mean ROUGE- L F1 score of .80 and can classify events with a macro F1 score of .79.Out of the 50 decision letters used for testing (each letter containing one timeline), the model correctly classified 10 of the timelines completely correct, with an average of 3.1 mistakes per decision letter.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Natural Language Processing
🧭 Keyword Pioneer — date extraction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio