Neural Open Information Extraction

Lei Cui; Furu Wei; Ming Zhou

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arXiv · 1805.04270

Neural Open Information Extraction

Lei Cui·Furu Wei·Ming Zhou

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Abstract

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.

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BibTeX RIS

Lei Cui, Furu Wei, Ming Zhou. 2018-05-11. Neural Open Information Extraction. https://arxiv.org/abs/1805.04270

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