A queueing-theory model of word frequency distributions

This paper describes a novel model for term frequency distributions that is derived from queuing-theory. It is compared with Poisson distributions, in terms of how well the models describe the observed distributions of terms, and it is demonstrated that a model for term frequency distributions based on queue utilisation generally gives a better fit. It is further demonstrated that the ratio of the fit/error between the Poisson and queue utilisation distributions may be used as a good indication of how interesting a word is in relation to the topic of the discourse. A number of possible reasons for this are discussed.