@inproceedings{barbosa-etal-2019-enabling,
title = "Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text",
author = "Barbosa, George C. G. and
Wong, Zechy and
Hahn-Powell, Gus and
Bell, Dane and
Sharp, Rebecca and
Valenzuela-Esc{\'a}rcega, Marco A. and
Surdeanu, Mihai",
editor = "Ammar, Waleed and
Louis, Annie and
Mostafazadeh, Nasrin",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-4003",
doi = "10.18653/v1/N19-4003",
pages = "12--17",
abstract = "Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multi-domain extractions of causal interactions into a single searchable knowledge graph. Our system enables users to search iteratively over direct and indirect connections in this knowledge graph, and collaboratively build causal models in real time. To enable the aggregation of causal information from multiple languages, we extend an open-domain machine reader to Portuguese. The new Portuguese reader extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62{\%}, which demonstrates the value of mining multilingual scientific information.",
}
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<abstract>Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multi-domain extractions of causal interactions into a single searchable knowledge graph. Our system enables users to search iteratively over direct and indirect connections in this knowledge graph, and collaboratively build causal models in real time. To enable the aggregation of causal information from multiple languages, we extend an open-domain machine reader to Portuguese. The new Portuguese reader extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62%, which demonstrates the value of mining multilingual scientific information.</abstract>
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%0 Conference Proceedings
%T Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text
%A Barbosa, George C. G.
%A Wong, Zechy
%A Hahn-Powell, Gus
%A Bell, Dane
%A Sharp, Rebecca
%A Valenzuela-Escárcega, Marco A.
%A Surdeanu, Mihai
%Y Ammar, Waleed
%Y Louis, Annie
%Y Mostafazadeh, Nasrin
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F barbosa-etal-2019-enabling
%X Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multi-domain extractions of causal interactions into a single searchable knowledge graph. Our system enables users to search iteratively over direct and indirect connections in this knowledge graph, and collaboratively build causal models in real time. To enable the aggregation of causal information from multiple languages, we extend an open-domain machine reader to Portuguese. The new Portuguese reader extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62%, which demonstrates the value of mining multilingual scientific information.
%R 10.18653/v1/N19-4003
%U https://aclanthology.org/N19-4003
%U https://doi.org/10.18653/v1/N19-4003
%P 12-17
Markdown (Informal)
[Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text](https://aclanthology.org/N19-4003) (Barbosa et al., NAACL 2019)
ACL