Low Resource Information Extraction

Low-resource information extraction (LRIE) focuses on developing methods to extract structured information from text with limited labeled data, a crucial challenge in natural language processing. Current research emphasizes data augmentation techniques, including targeted approaches and large language model-based synthesis of training data, to improve model performance. These advancements leverage techniques like reinforcement learning to refine pseudo-labeled data and address biases inherent in existing methods, ultimately aiming to improve the efficiency and scalability of information extraction across various tasks like named entity recognition, relation extraction, and event extraction. The success of LRIE will significantly impact fields relying on automated information processing from diverse and often limited sources.