  We propose a method to improve traditional character-based PPM text
compression algorithms. Consider a text file as a sequence of alternating words
and non-words, the basic idea of our algorithm is to encode non-words and
prefixes of words using character-based context models and encode suffixes of
words using dictionary models. By using dictionary models, the algorithm can
encode multiple characters as a whole, and thus enhance the compression
efficiency. The advantages of the proposed algorithm are: 1) it does not
require any text preprocessing; 2) it does not need any explicit codeword to
identify switch between context and dictionary models; 3) it can be applied to
any character-based PPM algorithms without incurring much additional
computational cost. Test results show that significant improvements can be
obtained over character-based PPM, especially in low order cases.
