Abstract
We present a system capable of modeling human newspaper readers. It is based on the extraction of reported speech, which is subsequently converted into a fuzzy theory-based representation of single statements. A domain analysis then assigns statements to topics. A number of fuzzy set operators, including fuzzy belief revision, are applied to model different belief strategies. At the end, our system holds certain beliefs while rejecting others.
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Proc. of the Human Language Technology Conference of the NAACL, New York City, USA, Association for Computational Linguistics (June 2006)
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Krestel, R., Witte, R., Bergler, S. (2007). Creating a Fuzzy Believer to Model Human Newspaper Readers. In: Kobti, Z., Wu, D. (eds) Advances in Artificial Intelligence. Canadian AI 2007. Lecture Notes in Computer Science(), vol 4509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72665-4_42
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DOI: https://doi.org/10.1007/978-3-540-72665-4_42
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