BIB-VERSION:: CS-TR-v2.0
ID:: ncstrl.dartmouthcs//TR99-357
ENTRY:: June 10, 1999
ORGANIZATION:: Dartmouth College, Computer Science
TITLE:: Investigating Measures for Pairwise Document Similarity
TYPE:: Technical Report (paper)
REVISION:: 1
AUTHOR:: Isaacs, Jeffrey D. 
AUTHOR:: Aslam, Javed A. 
DATE:: June 1999 
RETRIEVAL:: For a paper copy, email <reports@cs.dartmouth.edu>
RETRIEVAL:: For a paper copy, write to
       Technical Report Librarian
       Department of Computer Science
       Dartmouth College
       6211 Sudikoff Laboratory
       Hanover, NH 03755-3510
       USA
RETRIEVAL:: Compressed Postscript at http://www.cs.dartmouth.edu/reports/TR99-357.ps.Z
RETRIEVAL:: PDF at http://www.cs.dartmouth.edu/reports/TR99-357.pdf
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.
END:: ncstrl.dartmouthcs//TR99-357