  Most previous work on the recently developed language-modeling approach to
information retrieval focuses on document-specific characteristics, and
therefore does not take into account the structure of the surrounding corpus.
We propose a novel algorithmic framework in which information provided by
document-based language models is enhanced by the incorporation of information
drawn from clusters of similar documents. Using this framework, we develop a
suite of new algorithms. Even the simplest typically outperforms the standard
language-modeling approach in precision and recall, and our new interpolation
algorithm posts statistically significant improvements for both metrics over
all three corpora tested.
