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Query Expansion with ConceptNet and WordNet: An Intrinsic Comparison

  • Ming-Hung Hsu
  • Ming-Feng Tsai
  • Hsin-Hsi Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4182)

Abstract

This paper compares the utilization of ConceptNet and WordNet in query expansion. Spreading activation selects candidate terms for query expansion from these two resources. Three measures including discrimination ability, concept diversity, and retrieval performance are used for comparisons. The topics and document collections in the ad hoc track of TREC-6, TREC-7 and TREC-8 are adopted in the experiments. The results show that ConceptNet and WordNet are complementary. Queries expanded with WordNet have higher discrimination ability. In contrast, queries expanded with ConceptNet have higher concept diversity. The performance of queries expanded by selecting the candidate terms from ConceptNet and WordNet outperforms that of queries without expansion, and queries expanded with a single resource.

Keywords

Average Precision Retrieval Performance Query Term Query Expansion Discrimination Ability 
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 2006

Authors and Affiliations

  • Ming-Hung Hsu
    • 1
  • Ming-Feng Tsai
    • 1
  • Hsin-Hsi Chen
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan

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