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Temporal Pseudo-relevance Feedback in Microblog Retrieval

  • Stewart Whiting
  • Iraklis A. Klampanos
  • Joemon M. Jose
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)

Abstract

Twitter has become a major outlet for news, discussion and commentary of on-going events and trends. Effective searching of Twitter collections poses a number of issues for traditional document-based information retrieval (IR) approaches, such as limited document term statistics and spam. In this paper we propose a novel approach to pseudo-relevance feedback, based upon the temporal profiles of n-grams extracted from the top N relevance feedback tweets. A weighted graph is used to model temporal correlation between n-grams, with a PageRank variant employed to combine both pseudo-relevant document term distribution and temporal collection evidence. Preliminary experiments with the TREC Microblogging 2011 Twitter corpus indicate that through parameter optimisation, retrieval effectiveness can be improved.

Keywords

Query Term Query Expansion Microblog Post Linear Time Series Model Temporal Evidence 
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 2012

Authors and Affiliations

  • Stewart Whiting
    • 1
  • Iraklis A. Klampanos
    • 1
  • Joemon M. Jose
    • 1
  1. 1.School of Computing ScienceUniversity of GlasgowScotland, UK

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