Better than the real thing?: iterative pseudo-query processing using cluster-based language models

  • Authors:

  • Oren Kurland;Lillian Lee;Carmel Domshlak

  • Affiliations:

  • Cornell University, Ithaca NY and Carnegie Mellon University, Pittsburgh PA;Cornell University, Ithaca NY and Carnegie Mellon University, Pittsburgh PA;Technion, Haifa, Israel

  • Venue:
  • Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2005

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Abstract

We present a novel approach to pseudo-feedback-based ad hoc retrieval that uses language models induced from both documents and clusters. First, we treat the pseudo-feedback documents produced in response to the original query as a set of pseudo-query that themselves can serve as input to the retrieval process. Observing that the documents returned in response to the pseudo-query can then act as pseudo-query for subsequent rounds, we arrive at a formulation of pseudo-query-based retrieval as an iterative process. Experiments show that several concrete instantiations of this idea, when applied in conjunction with techniques designed to heighten precision, yield performance results rivaling those of a number of previously-proposed algorithms, including the standard language-modeling approach. The use of cluster-based language models is a key contributing factor to our algorithms' success.