Automatic Concept Acquisition from Real-World Texts
Udo Hahn, Manfred Klenner · 2002
We introduce a concept learning methodology for text understanding systems that is based on terminological knowledge representation and reasoning. Quality-based metareasoning techniques allow for an incremental evaluation and selection of concept hypotheses. This methodology is particularly aimed at real-world text understanding environments where lexical/conceptual resources cannot be completely specified prior to text analysis and, as a consequence of partial understanding, competing concept hypotheses with different levels of credibility have to be managed. Appeared in: MLIA'96 - Working Notes of the AAAI-96 Spring Symposium on 'Machine Learning in Information Access', Stanford University, Stanford, Calif., March 25-27, 1996, American Association for Artificial Intelligence, pp.104-106 (AAAI-96 Spring Symposium Series). Automatic Concept Acquisition from Real-World Texts Udo Hahn, Manfred Klenner & Klemens Schnattinger Freiburg University, L F Computational Linguistics Group ...