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Relevance data for language models using maximum likelihood

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  • David Bodoff
  • Bin Wu
  • K. Y. Michael Wong

Abstract

We present a preliminary empirical test of a maximum likelihood approach to using relevance data for training information retrieval (IR) parameters. Similar to language models, our method uses explicitly hypothesized distributions for documents and queries, but we add to this an explicitly hypothesized distribution for relevance judgments. The method unifies document‐oriented and query‐oriented views. Performance is better than the Rocchio heuristic for document and/or query modification. The maximum likelihood methodology also motivates a heuristic estimate of the MLE optimization. The method can be used to test competing hypotheses regarding the processes of authors' term selection, searchers' term selection, and assessors' relevancy judgments.

Suggested Citation

  • David Bodoff & Bin Wu & K. Y. Michael Wong, 2003. "Relevance data for language models using maximum likelihood," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(11), pages 1050-1061, September.
    • Handle: RePEc:bla:jamist:v:54:y:2003:i:11:p:1050-1061
    DOI: 10.1002/asi.10300
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