@CONFERENCE\{IMM2005-03996,
    author       = "R. E. Madsen and D. Kauchak and C. Elkan",
    title        = "Modeling Word Burstiness Using the Dirichlet Distribution",
    year         = "2005",
    month        = "jun",
    keywords     = "Text mining, {DCM,} Polya, Multinomial, Categorization, Supervised",
    pages        = "489--498",
    booktitle    = "International Conference on Machine Learning",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/3996-full.html",
    abstract     = "Multinomial distributions are often used to
model text documents. However, they do
not capture well the phenomenon that words
in a document tend to appear in bursts: if
a word appears once, it is more likely to
appear again. In this paper, we propose
the Dirichlet compound multinomial model
(DCM) as an alternative to the multinomial.
The {DCM} model has one additional degree
of freedom, which allows it to capture burstiness.
We show experimentally that the {DCM} 
is substantially better than the multinomial
at modeling text data, measured by perplexity.
We also show using three standard document
collections that the {DCM} leads to better
classification than the multinomial model.
{DCM} performance is comparable to that obtained
with multiple heuristic changes to the
multinomial model."
}