By Topic

Analysis of combining multiple query representations with varying lengths in a single engine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)

We examine the issues of combining multiple query representations in a single IR engine. Differing query representations are used to retrieve different documents. Thus, when combining their results, improvements are observed in effectiveness. We use multiple TREC query representations (title, description and narrative) as a basis for experimentation. We examine several combination approaches presented in the literature (vector addition, CombSUM and CombMNZ) and present a new combination approach using query vector length normalization. We examine two query representation combination approaches (title + description and title + narrative) for 150 queries from TREC 6, 7 and 8 topics. Our QLN (Query Length Normalization) technique outperformed vector addition and data fusion approaches by as much as 32% and was on average 24% better. Additionally, QLN always outperformed the single best query representation in terms of effectiveness.

Published in:

Information Technology: Coding and Computing, 2002. Proceedings. International Conference on

Date of Conference:

8-10 April 2002