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Abstract

Multiple similarity measures for five TREC topic-document sets from the LDC TREC Collection Disk 1 are derived from the full text of documents. Each measure on each set is scaled using SAS MDS under ordinal, interval, and MLE assumptions. The resulting 75 permutations are ploted. It is suggested that cosine-vector and overlap measures for similarity appear to recover optimal data relationships among the documents of the five sets. MLE assumptions appear to be required to model the data adequately.