Neural Open Information Extraction
Lei Cui, Furu Wei, Ming Zhou . Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2018 – 131 citations
Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.