LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction

Jacob Solawetz, Stefan Larson


Abstract
Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailment, and natural language understanding. However, current OIE datasets are limited in both size and diversity. We introduce a new dataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset LSOIE. Our LSOIE dataset is 20 times larger than the next largest human-annotated OIE dataset. We construct and evaluate several benchmark OIE models on LSOIE, providing baselines for future improvements on the task. Our LSOIE data, models, and code are made publicly available.
Anthology ID:
2021.eacl-main.222
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2595–2600
Language:
URL:
https://aclanthology.org/2021.eacl-main.222
DOI:
10.18653/v1/2021.eacl-main.222
Bibkey:
Cite (ACL):
Jacob Solawetz and Stefan Larson. 2021. LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2595–2600, Online. Association for Computational Linguistics.
Cite (Informal):
LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction (Solawetz & Larson, EACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2021.eacl-main.222.pdf
Dataset:
 2021.eacl-main.222.Dataset.zip
Software:
 2021.eacl-main.222.Software.zip
Code
 Jacobsolawetz/large-scale-oie