Comparison of New Simple Weighting Functions for Web Documents against Existing Methods

  • Byurhan Hyusein
  • Ahmed Patel
  • Ferad Zyulkyarov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2869)


Term weighting is one of the most important aspects of modern Web retrieval systems. The weight associated with a given term in a document shows the importance of the term for the document, i.e. its usefulness for distinguishing documents in a document collection. In search engines operating in a dynamic environment such as the Internet, where many documents are deleted from and added to the database, the usual formula involving the inverse document frequency is too costly to be computed each time the document collection is updated. This paper proposes two new simple and effective weighting functions. These weighting functions have been tested and compared with results obtained for the PIVOT, SMART and INQUERY methods using the WT10g collection of documents.


Weighting Function Relevant Document Average Precision Term Frequency Document Frequency 
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 2003

Authors and Affiliations

  • Byurhan Hyusein
    • 1
  • Ahmed Patel
    • 1
  • Ferad Zyulkyarov
    • 1
  1. 1.Computer Networks and Distributed Systems Research Group, Department of Computer ScienceUniversity College DublinBelfield, Dublin 4Ireland

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