Abstract
We introduce a methodology for concept learning from texts that relies upon second-order reasoning about statements expressed in a (first-order) terminological representation language. This metareasoning approach allows for quality-based evaluation and selection of alternative concept hypotheses.
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Hahn, U., Klenner, M., Schnattinger, K. (1996). Learning from texts — A terminological metareasoning perspective. In: Wermter, S., Riloff, E., Scheler, G. (eds) Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. IJCAI 1995. Lecture Notes in Computer Science, vol 1040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60925-3_66
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DOI: https://doi.org/10.1007/3-540-60925-3_66
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