Neural OpenIE

Neural Open Information Extraction (OpenIE) aims to automatically extract relational facts from text, a crucial step for building knowledge bases and powering downstream NLP applications. Recent research emphasizes improving the generalization capabilities of OpenIE models across diverse domains, exploring novel architectures like graph-based representations and multi-view learning to better leverage syntactic information. This focus on robustness and efficiency, including efforts to generate more compact and reusable extractions, is driven by the need for more reliable and practical OpenIE systems for real-world applications.