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IPC Selection Using Collection Selection Algorithms

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Multidisciplinary Information Retrieval (IRFC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8849))

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Abstract

In this paper we view the automated selection of patent classification codes as a collection selection problem that can be addressed using existing methods which we extend and adapt for the patent domain. Our work exploits the manually assigned International Patent Classification (IPC) codes of patent documents to cluster, distribute and index patents through hundreds or thousands of sub-collections. We examine different collection selection methods (CORI, Bordafuse, ReciRank and multilayer) and compare their effectiveness in selecting relevant IPCs. The multilayer method, in addition to utilizing the topical relevance of IPCs at a specific level (e.g. sub-class), exploits the topical relevance of their ancestors in the IPC hierarchy and aggregates those multiple estimations of relevance to a single estimation. The results show that multilayer outperforms CORI and fusion-based methods in the task of IPC suggestion.

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Author information

Authors and Affiliations

  1. Faculty of Informatics, University of Lugano, Lugano, Switzerland

    Anastasia Giachanou

  2. Department of Informatics, Alexander Technological Educational Institute of Thessaloniki, Thessaloniki, Greece

    Michail Salampasis

Authors
  1. Anastasia Giachanou
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  2. Michail Salampasis
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Editor information

Editors and Affiliations

  1. Institute of Informatics, Tallinn University, Narva mnt 25, Tallinn, Estonia

    David Lamas

  2. Insight Centre for Data Analytics, Unit for Natural Language Processing, IDA, National University of Ireland, Business Park, Lower Dangan, Galway, Ireland

    Paul Buitelaar

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© 2014 Springer International Publishing Switzerland

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Giachanou, A., Salampasis, M. (2014). IPC Selection Using Collection Selection Algorithms. In: Lamas, D., Buitelaar, P. (eds) Multidisciplinary Information Retrieval. IRFC 2014. Lecture Notes in Computer Science, vol 8849. Springer, Cham. https://doi.org/10.1007/978-3-319-12979-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-12979-2_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12978-5

  • Online ISBN: 978-3-319-12979-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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