Integrating Background Knowledge into Nearest-Neighbor Text Classification

  • Sarah Zelikovitz
  • Haym Hirsh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2416)


This paper describes two different approaches for incorporating background knowledge into nearest-neighbor text classification. Our first approach uses background text to assess the similarity between training and test documents rather than assessing their similarity directly. The second method redescribes examples using Latent Semantic Indexing on the background knowledge, assessing document similarities in this redescribed space. Our experimental results show that both approaches can improve the performance of nearest-neighbor text classification. These methods are especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a specific problem on the World Wide Web.


Background Knowledge Test Document Latent Semantic Analysis Latent Semantic Indexing Label Training 
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 2002

Authors and Affiliations

  • Sarah Zelikovitz
    • 1
  • Haym Hirsh
    • 1
  1. 1.Computer Science DepartmentRutgers UniversityPiscataway

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