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.
@article{arxiv.1805.04270,
title = {Neural Open Information Extraction},
author = {Lei Cui and Furu Wei and Ming Zhou},
journal= {arXiv preprint arXiv:1805.04270},
year = {2018}
}