Combining Rule-Based and Machine Learning Methods for Efficient Information Extraction from Enforcement Decisions
Harry Nan, M. Marx, Johan Wolswinkel · Frontiers in artificial intelligence and applications · 2024
This paper presents an effective and efficient approach for automatic extraction of key features from enforcement decisions, such as their legal basis and their legal effect, by strategically applying a Large Language Model (LLM) on top of rule-based methods. Initially, rule-based methods identify candidate sentences within these decisions containing these features, after which these sentences are analyzed by GPT-3.5 to extract the features. This approach is efficient as it reduces the input and number of resources needed for effective and context aware information extraction. Furthermore, other features that have not been subject to a rule-based selection first can be extracted by an LLM from the same set of candidate sentences when they exist in close proximity of each other.