A Field Relevance Model for Structured Document Retrieval

  • Jin Young Kim
  • W. Bruce Croft
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)


Many search applications involve documents with structure or fields. Since query terms often are related to specific structural components, mapping queries to fields and assigning weights to those fields is critical for retrieval effectiveness. Although several field-based retrieval models have been developed, there has not been a formal justification of field weighting.

In this work, we aim to improve the field weighting for structured document retrieval. We first introduce the notion of field relevance as the generalization of field weights, and discuss how it can be estimated using relevant documents, which effectively implements relevance feedback for field weighting. We then propose a framework for estimating field relevance based on the combination of several sources. Evaluation on several structured document collections show that field weighting based on the suggested framework improves retrieval effectiveness significantly.


Relevant Document Relevance Feedback Retrieval Model Query Term Mean Average Precision 
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

  • Jin Young Kim
    • 1
  • W. Bruce Croft
    • 1
  1. 1.Center for Intelligent Information Retrieval, Department of Computer ScienceUniversity of MassachusettsAmherstUSA

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