Abstract: Keyword frequency data for a plurality of document-derived segments is represented in a matrix form in which each segment is represented as a vector of dimensionality equal to the number of keywords. The matrix may be subdivided into a plurality of sub-matrices, each preferably corresponding to a non-overlapping portion of the plurality of keywords. When determining a similarity measurement between any pair of segments, at least a portion of the keyword frequency data for each sub-matrix's non-overlapping keywords are used to determine a sub-matrix dot product for the pair of segments. The resulting plurality of sub-matrix dot products are then summed together in order to provide the similarity measurement. Keywords that are synonyms of each other may be accommodated through the modification of keyword frequency data. Where the keyword frequency data in the matrix representation is relative sparse, compressed views of the matrix representation may be provided.

Abstract: Keyword frequency data for a plurality of document-derived segments is represented in a matrix form in which each segment is represented as a vector of dimensionality equal to the number of keywords. The matrix may be subdivided into a plurality of sub-matrices, each preferably corresponding to a non-overlapping portion of the plurality of keywords. When determining a similarity measurement between any pair of segments, at least a portion of the keyword frequency data for each sub-matrix's non-overlapping keywords are used to determine a sub-matrix dot product for the pair of segments. The resulting plurality of sub-matrix dot products are then summed together in order to provide the similarity measurement. Keywords that are synonyms of each other may be accommodated through the modification of keyword frequency data. Where the keyword frequency data in the matrix representation is relative sparse, compressed views of the matrix representation may be provided.