@inproceedings{sharp-etal-2019-eidos,
title = "Eidos, {INDRA}, {\&} Delphi: From Free Text to Executable Causal Models",
author = "Sharp, Rebecca and
Pyarelal, Adarsh and
Gyori, Benjamin and
Alcock, Keith and
Laparra, Egoitz and
Valenzuela-Esc{\'a}rcega, Marco A. and
Nagesh, Ajay and
Yadav, Vikas and
Bachman, John and
Tang, Zheng and
Lent, Heather and
Luo, Fan and
Paul, Mithun and
Bethard, Steven and
Barnard, Kobus and
Morrison, Clayton 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/N19-4008",
doi = "10.18653/v1/N19-4008",
pages = "42--47",
abstract = "Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.",
}
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<abstract>Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.</abstract>
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%0 Conference Proceedings
%T Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models
%A Sharp, Rebecca
%A Pyarelal, Adarsh
%A Gyori, Benjamin
%A Alcock, Keith
%A Laparra, Egoitz
%A Valenzuela-Escárcega, Marco A.
%A Nagesh, Ajay
%A Yadav, Vikas
%A Bachman, John
%A Tang, Zheng
%A Lent, Heather
%A Luo, Fan
%A Paul, Mithun
%A Bethard, Steven
%A Barnard, Kobus
%A Morrison, Clayton
%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 sharp-etal-2019-eidos
%X Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.
%R 10.18653/v1/N19-4008
%U https://aclanthology.org/N19-4008
%U https://doi.org/10.18653/v1/N19-4008
%P 42-47
Markdown (Informal)
[Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models](https://aclanthology.org/N19-4008) (Sharp et al., NAACL 2019)
ACL
- Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Escárcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, and Mihai Surdeanu. 2019. Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 42–47, Minneapolis, Minnesota. Association for Computational Linguistics.