@inproceedings{a48e849b829b4dfa9ed3dbd81c999575,
title = "Open information extraction on scientific text: An evaluation",
abstract = "Open Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks including knowledge base construction, relation extraction, and question answering. While OIE methods are targeted at being domain independent, they have been evaluated primarily on newspaper, encyclopedic or general web text. In this article, we evaluate the performance of OIE on scientific texts originating from 10 different disciplines. To do so, we use two state-of-the-art OIE systems using a crowd-sourcing approach. We find that OIE systems perform significantly worse on scientific text than encyclopedic text. We also provide an error analysis and suggest areas of work to reduce errors. Our corpus of sentences and judgments are made available.",
author = "Paul Groth and Mike Lauruhn and Antony Scerri and Ron Daniel",
note = "Publisher Copyright: {\textcopyright} 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.; 27th International Conference on Computational Linguistics, COLING 2018 ; Conference date: 20-08-2018 Through 26-08-2018",
year = "2018",
language = "Ingl{\'e}s",
series = "COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "3414--3423",
editor = "Bender, {Emily M.} and Leon Derczynski and Pierre Isabelle",
booktitle = "COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings",
address = "Estados Unidos",
}