  We introduce a generalised multivariate Polya process for document language
modelling. The framework outlined here generalises a number of statistical
language models used in information retrieval for modelling document
generation. In particular, we show that the choice of replacement matrix M
ultimately defines the type of random process and therefore defines a
particular type of document language model. We show that a particular variant
of the general model is useful for modelling term-specific burstiness.
Furthermore, via experimentation we show that this variant significantly
improves retrieval effectiveness over a strong baseline on a number of small
test collections.
