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        <title>Learning Causal Bayesian Networks from Text</title>
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        <namePart type="given">Farhad</namePart>
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    <name type="personal">
        <namePart type="given">Mahsa</namePart>
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            <title>Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association</title>
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            <namePart type="given">Maria</namePart>
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            <namePart type="given">Daniel</namePart>
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    <abstract>Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic discovery of causal relationships from text has emerged as a significant challenge in recent years. Existing approaches in this realm are limited to the extraction of low-level relations among individual events. To overcome the limitations of the existing approaches, in this paper, we propose a method for automatic inference of causal relationships from human written language at conceptual level. To this end, we leverage the characteristics of hierarchy of concepts and linguistic variables created from text, and represent the extracted causal relationships in the form of a Causal Bayesian Network. Our experiments demonstrate superiority of our approach over the existing approaches in inferring complex causal reasoning from the text.</abstract>
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%0 Conference Proceedings
%T Learning Causal Bayesian Networks from Text
%A Moghimifar, Farhad
%A Rahimi, Afshin
%A Baktashmotlagh, Mahsa
%A Li, Xue
%Y Kim, Maria
%Y Beck, Daniel
%Y Mistica, Meladel
%S Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association
%D 2020
%8 December
%I Australasian Language Technology Association
%C Virtual Workshop
%F moghimifar-etal-2020-learning
%X Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic discovery of causal relationships from text has emerged as a significant challenge in recent years. Existing approaches in this realm are limited to the extraction of low-level relations among individual events. To overcome the limitations of the existing approaches, in this paper, we propose a method for automatic inference of causal relationships from human written language at conceptual level. To this end, we leverage the characteristics of hierarchy of concepts and linguistic variables created from text, and represent the extracted causal relationships in the form of a Causal Bayesian Network. Our experiments demonstrate superiority of our approach over the existing approaches in inferring complex causal reasoning from the text.
%U https://aclanthology.org/2020.alta-1.9
%P 81-85

Markdown (Informal)

[Learning Causal Bayesian Networks from Text](https://aclanthology.org/2020.alta-1.9) (Moghimifar et al., ALTA 2020)

• Learning Causal Bayesian Networks from Text (Moghimifar et al., ALTA 2020)

ACL

• Farhad Moghimifar, Afshin Rahimi, Mahsa Baktashmotlagh, and Xue Li. 2020. Learning Causal Bayesian Networks from Text. In Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association, pages 81–85, Virtual Workshop. Australasian Language Technology Association.