Automatic Concept Acquisition from Real-World Texts

Machine Learning in Information Access

Volume

Papers from the 1996 AAAI Spring Symposium

Contents

We introduce a concept learning methodology for text understanding systems that is based on terminological knowledge representation and reasoning. Qualitybased metareasoning techniques allow for an incremental evaluation and selection of concept hypotheses. This methodology is particularly aimed at realworld 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.