In Layman’s Terms: Semi-Open Relation Extraction from Scientific Texts

Ruben Kruiper, Julian Vincent, Jessica Chen-Burger, Marc Desmulliez, Ioannis Konstas

Abstract
Information Extraction (IE) from scientific texts can be used to guide readers to the central information in scientific documents. But narrow IE systems extract only a fraction of the information captured, and Open IE systems do not perform well on the long and complex sentences encountered in scientific texts. In this work we combine the output of both types of systems to achieve Semi-Open Relation Extraction, a new task that we explore in the Biology domain. First, we present the Focused Open Biological Information Extraction (FOBIE) dataset and use FOBIE to train a state-of-the-art narrow scientific IE system to extract trade-off relations and arguments that are central to biology texts. We then run both the narrow IE system and a state-of-the-art Open IE system on a corpus of 10K open-access scientific biological texts. We show that a significant amount (65%) of erroneous and uninformative Open IE extractions can be filtered using narrow IE extractions. Furthermore, we show that the retained extractions are significantly more often informative to a reader.
Anthology ID:
2020.acl-main.137
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1489–1500
Language:
URL:
https://aclanthology.org/2020.acl-main.137
DOI:
10.18653/v1/2020.acl-main.137
Bibkey:
Cite (ACL):
Ruben Kruiper, Julian Vincent, Jessica Chen-Burger, Marc Desmulliez, and Ioannis Konstas. 2020. In Layman’s Terms: Semi-Open Relation Extraction from Scientific Texts. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1489–1500, Online. Association for Computational Linguistics.
Cite (Informal):
In Layman’s Terms: Semi-Open Relation Extraction from Scientific Texts (Kruiper et al., ACL 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.137.pdf
Video:
 http://slideslive.com/38928870
Code
 rubenkruiper/FOBIE
Data
FOBIE, BB
PDF Search Code Video