Abstract
Human actions in this world are based on exploiting knowledge of causality. Humans find it easy to connect a cause to the subsequent effect but formal reasoning about causality has proved to be a difficult task in automated NLP applications because it requires rich knowledge of all the relevant events and circumstances. Automated approaches to detecting causal connections attempt to partially capture this knowledge using commonsense reasoning based on lexical and semantics constraints. However, their performance is limited by the lack of sufficient breadth of commonsense knowledge to draw causal inferences. This paper presents a commonsense causality detection system using a new semantic measure based on asymmetric associations on the Choice Of Plausible Alternatives (COPA) task. When evaluated on three COPA benchmark datasets, the causality detection system using asymmetric association based measures demonstrates a superior performance to other symmetric measures.
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References
Davidson, D.: Actions, reasons, and causes. The Journal of Philosophy 60(23), 685–700 (1963)
Gordon, A.S., Bejan, C.A., Sagae, K.: Commonsense causal reasoning using millions of personal stories. In: Proceedings of Twenty Fifth Conference on Artificial Intelligence, AAAI 2011 (2011)
Hart, H.L.A., Honoré, T.: Causation in the Law. Oxford University Press (1985)
Hobbs, J.R.: Toward a useful concept of causality for lexical semantics. Journal of Semantics 22(2), 181–209 (2005)
Roemmele, M., Bejan, C.A., Gordon, A.S.: Choice of plausible alternatives: An evaluation of commonsense causal reasoning. In: AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning (2011)
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Jabeen, S., Gao, X., Andreae, P. (2014). Using Asymmetric Associations for Commonsense Causality Detection. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_73
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DOI: https://doi.org/10.1007/978-3-319-13560-1_73
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
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