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Improving Document Transformation Techniques with Collaborative Learned Term-Based Concepts

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Reading and Learning

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2956))

Abstract

Document Transformation techniques have been studied for decades. In this paper, a new approach for a significant improvement is presented based on using a new query expansion method. In contrast to other methods, the regarded query is expanded by adding those terms that are most similar to the concept of individual query terms, rather than selecting terms that are similar to the complete query or that are directly similar to the query terms. Experiments have shown that Document Transformation techniques are significantly improved in the retrieval effectiveness when measuring the recall-precision.

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Authors and Affiliations

  1. Department of Database and Information Systems (DBIS), University of Trier, D–54286, Trier, Germany

    Stefan Klink

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Editors and Affiliations

  1. Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany

    Andreas Dengel

  2. German Research Center for Artificial Intelligence (DFKI), P.O.Box 2080, 67608, Kaiserslautern, Germany

    Markus Junker

  3. Institute for Human Factors and Technology Management, University of Stuttgart, Nobelstraße 12, 70569, Stuttgart, Germany

    Anette Weisbecker

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© 2004 Springer-Verlag Berlin Heidelberg

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Klink, S. (2004). Improving Document Transformation Techniques with Collaborative Learned Term-Based Concepts. In: Dengel, A., Junker, M., Weisbecker, A. (eds) Reading and Learning. Lecture Notes in Computer Science, vol 2956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24642-8_16

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  • DOI: https://doi.org/10.1007/978-3-540-24642-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21904-0

  • Online ISBN: 978-3-540-24642-8

  • eBook Packages: Springer Book Archive

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