Abstract
We describe statistical models for detecting causality between two events. Our models are kinds of latent variable models, actually expanded versions of the existing statistical co-occurrence models. The (statistical) dependency information between two events needs to be incorporated into causal models. We handle this information via latent variables in our models. Through experiments, we achieved .678 F-measure value for the evaluation data.
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Inui, T., Takamura, H., Okumura, M. (2007). Latent Variable Models for Causal Knowledge Acquisition. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2007. Lecture Notes in Computer Science, vol 4394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70939-8_8
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DOI: https://doi.org/10.1007/978-3-540-70939-8_8
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