Cardinal Virtues: Extracting Relation Cardinalities from Text

Paramita Mirza, Simon Razniewski, Fariz Darari, Gerhard Weikum


Abstract
Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.
Anthology ID:
P17-2055
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
347–351
Language:
URL:
https://aclanthology.org/P17-2055
DOI:
10.18653/v1/P17-2055
Bibkey:
Cite (ACL):
Paramita Mirza, Simon Razniewski, Fariz Darari, and Gerhard Weikum. 2017. Cardinal Virtues: Extracting Relation Cardinalities from Text. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 347–351, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Cardinal Virtues: Extracting Relation Cardinalities from Text (Mirza et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2055.pdf
Presentation:
 P17-2055.Presentation.pdf
Dataset:
 P17-2055.Datasets.zip
Data
YAGO