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Investigating Measures for Pairwise Document Similarity
Jeffrey D. Isaacs, Javed A. Aslam
Dartmouth PCS-TR99-357

Abstract: The need for a more effective similarity measure is growing as a result of the astonishing amount of information being placed online. Most existing similarity measures are defined by empirically derived formulas and cannot easily be extended to new applications. We present a pairwise document similarity measure based on Information Theory, and present corpus dependent and independent applications of this measure. When ranked with existing similarity measures over TREC FBIS data, our corpus dependent information theoretic similarity measure ranked first.

Note: Undergraduate Honors Thesis. Advisor: Jay Aslam.

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   Jeffrey D. Isaacs and Javed A. Aslam, "Investigating Measures for Pairwise Document Similarity." Dartmouth Computer Science Technical Report PCS-TR99-357, June 1999.

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