TY - GEN
T1 - Enabling search and collaborative assembly of causal interactions extracted from multilingual and multi-domain free text
AU - Barbosa, George C.G.
AU - Wong, Zechy
AU - Hahn-Powell, Gus
AU - Bell, Dane
AU - Sharp, Rebecca
AU - Valenzuela-Escarcega, Marco A.
AU - Surdeanu, Mihai
N1 - Funding Information:
This work was funded by the Bill and Melinda Gates Foundation HBGDki Initiative. Marco Valenzuela-Escárcega and Mihai Surdeanu declare a financial interest in LUM.AI. This interest has been properly disclosed to the University of Arizona Institutional Review Committee and is managed in accordance with its conflict of interest policies.
Publisher Copyright:
© 2019 The Association for Computational Linguistics.
PY - 2019
Y1 - 2019
N2 - 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 multidomain 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.
AB - 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 multidomain 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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M3 - Conference contribution
AN - SCOPUS:85085641989
T3 - NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Demonstrations Session
SP - 12
EP - 17
BT - NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
Y2 - 2 June 2019 through 7 June 2019
ER -