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Improving Access to Large Patent Corpora

  • Richard Bache
  • Leif Azzopardi
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6380)

Abstract

Retrievability is a measure of access that quantifies how easily documents can be found using a retrieval system. Such a measure is of particular interest within the patent domain, because if a retrieval system makes some patents hard to find, then patent searchers will have a difficult time retrieving these patents. This may mean that a patent searcher could miss important and relevant patents because of the retrieval system. In this paper, we describe measures of retrievability and how they can be applied to measure the overall access to a collection given a retrieval system. We then identify three features of best-match retrieval models that are hypothesized to lead to an improvement in access to all documents in the collection: sensitivity to term frequency, length normalization and convexity. Since patent searchers tend to favor Boolean models over best-match models, hybrid retrieval models are proposed that incorporate these features while preserving the desirable aspects of the traditional Boolean model. An empirical study conducted on four large patent corpora demonstrates that these hybrid models provide better access to the corpus of patents than the traditional Boolean model.

Keywords

Term Frequency Lorenz Curve Retrieval Model Query Term Information Retrieval System 
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 2010

Authors and Affiliations

  • Richard Bache
    • 1
  • Leif Azzopardi
    • 1
  1. 1.Department of Computing ScienceUniversity of GlasgowGlasgowUK

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