Using temporal bursts for query modeling

  • Authors:

  • Maria-Hendrike Peetz;Edgar Meij;Maarten Rijke

  • Affiliations:

  • ISLA, University of Amsterdam, Amsterdam, The Netherlands;ISLA, University of Amsterdam, Amsterdam, The Netherlands;ISLA, University of Amsterdam, Amsterdam, The Netherlands

  • Venue:
  • Information Retrieval
  • Year:
  • 2014

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Abstract

We present an approach to query modeling that leverages the temporal distribution of documents in an initially retrieved set of documents. In news-related document collections such distributions tend to exhibit bursts. Here, we define a burst to be a time period where unusually many documents are published. In our approach we detect bursts in result lists returned for a query. We then model the term distributions of the bursts using a reduced result list and select its most descriptive terms. Finally, we merge the sets of terms obtained in this manner so as to arrive at a reformulation of the original query. For query sets that consist of both temporal and non-temporal queries, our query modeling approach incorporates an effective selection method of terms. We consistently and significantly improve over various baselines, such as relevance models, on both news collections and a collection of blog posts.