2019 ACL ACL 2019

WiRe57 : A Fine-Grained Benchmark for Open Information Extraction

Abstract

AbstractWe build a reference for the task of Open Information Extraction, on five documents. We tentatively resolve a number of issues that arise, including coreference and granularity, and we take steps toward addressing inference, a significant problem. We seek to better pinpoint the requirements for the task. We produce our annotation guidelines specifying what is correct to extract and what is not. In turn, we use this reference to score existing Open IE systems. We address the non-trivial problem of evaluating the extractions produced by systems against the reference tuples, and share our evaluation script. Among seven compared extractors, we find the MinIE system to perform best.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — tuple 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