Selection fusion in semi-structured retrieval

  • Authors:

  • Muhammad Ali Norozi;Paavo Arvola

  • Affiliations:

  • Norwegian University of Science and Technology, Trondheim, Norway;University of Tampere, Tampere, Finland

  • Venue:
  • Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Semi-structured retrieval aims at providing focused answers to the users queries. A successful retrieval experience in semi-structured environment would mean a satisfactory combination of (a) matching or scoring and (b) selection of appropriate and focused fragments of the text. The need to retrieve items of different sizes arises today with users querying the retrieval systems with varied use case, user interface and screen-size requirements. Which means that different selection scenario serve different requirements and constraints. Hence we propose, a novel type of fusion; the \textit{selection fusion} -- a fusion methodology which fuses an all-purpose and comprehensive ranking of elements with a specific selection scheme, and also enables evaluation of the ranking in many selection perspectives. With the standard Wikipedia XML test collection, we are able to demonstrate that a strong and competitive baseline ranking system improves retrieval quality irrespective of the selection criteria. Our baseline ranking system is based on data fusion over the official submitted runs at INEX 2009.