@inproceedings{93564e61593246a6b5884f8bf3e4cc1f,
title = "Supervised open information extraction",
abstract = "We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model, made publicly available, 1 outperforms the state-of-The-Art in Open IE on benchmark datasets.",
author = "Gabriel Stanovsky and Julian Michael and Luke Zettlemoyer and Ido Dagan",
note = "Publisher Copyright: {\textcopyright} 2018 The Association for Computational Linguistics.; 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 ; Conference date: 01-06-2018 Through 06-06-2018",
year = "2018",
language = "الإنجليزيّة",
series = "NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "885--895",
booktitle = "Long Papers",
address = "الولايات المتّحدة",
}