Open Information Extraction

Open Information Extraction (OIE) aims to automatically extract structured factual information, typically in the form of subject-relation-object triples, from unstructured text regardless of domain. Current research emphasizes improving accuracy and efficiency through various approaches, including rule-based systems, neural networks leveraging pre-trained language models (PLMs) and techniques inspired by object detection in computer vision, often incorporating linguistic features like part-of-speech tags and dependency parses. The field is crucial for building large-scale knowledge graphs, enhancing question answering systems, and improving various downstream NLP tasks, with ongoing efforts focused on addressing challenges like data bias, handling multilingual text, and ensuring robustness across diverse domains.