  Document coherence describes how much sense text makes in terms of its
logical organisation and discourse flow. Even though coherence is a relatively
difficult notion to quantify precisely, it can be approximated automatically.
This type of coherence modelling is not only interesting in itself, but also
useful for a number of other text processing tasks, including Information
Retrieval (IR), where adjusting the ranking of documents according to both
their relevance and their coherence has been shown to increase retrieval
effectiveness [34,37].
  The state of the art in unsupervised coherence modelling represents documents
as bipartite graphs of sentences and discourse entities, and then projects
these bipartite graphs into one-mode undirected graphs. However, one-mode
projections may incur significant loss of the information present in the
original bipartite structure. To address this we present three novel graph
metrics that compute document coherence on the original bipartite graph of
sentences and entities. Evaluation on standard settings shows that: (i) one of
our coherence metrics beats the state of the art in terms of coherence
accuracy; and (ii) all three of our coherence metrics improve retrieval
effectiveness because, as closer analysis reveals, they capture aspects of
document quality that go undetected by both keyword-based standard ranking and
by spam filtering. This work contributes document coherence metrics that are
theoretically principled, parameter-free, and useful to IR.
