@inproceedings{mirza-etal-2017-cardinal,
title = "Cardinal Virtues: Extracting Relation Cardinalities from Text",
author = "Mirza, Paramita and
Razniewski, Simon and
Darari, Fariz and
Weikum, Gerhard",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2055",
doi = "10.18653/v1/P17-2055",
pages = "347--351",
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.",
}
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%0 Conference Proceedings
%T Cardinal Virtues: Extracting Relation Cardinalities from Text
%A Mirza, Paramita
%A Razniewski, Simon
%A Darari, Fariz
%A Weikum, Gerhard
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F mirza-etal-2017-cardinal
%X 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.
%R 10.18653/v1/P17-2055
%U https://aclanthology.org/P17-2055
%U https://doi.org/10.18653/v1/P17-2055
%P 347-351
Markdown (Informal)
[Cardinal Virtues: Extracting Relation Cardinalities from Text](https://aclanthology.org/P17-2055) (Mirza et al., ACL 2017)
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.