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An empirical evaluation of a system for text knowledge acquisition

Long Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1319)

Abstract

We introduce a formal model and a corresponding system architecture for the acquisition of new concepts from real-world natural language texts. Our approach is centered around the linguistic and conceptual “quality” of various forms of evidence underlying the generation and refinement of concept hypotheses. Based on a terminological (meta)reasoning platform, hypotheses are continuously annotated by a stream of linguistic and conceptual evidence, preferentially ranked and, finally, selected according to their overall credibility. We discuss the results of an empirical evaluation study, concentrating on the system's learning rate and learning accuracy.

Keywords

Knowledge Acquisition Semantic Interpretation Hypothesis Space Learning Step Natural Language Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  1. 1.Text Knowledge Engineering LabFreiburg UniversityFreiburgGermany

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