  The need to organize a large collection in a manner that facilitates human
comprehension is crucial given the ever-increasing volumes of information. In
this work, we present PDC (probabilistic distributional clustering), a novel
algorithm that, given a document collection, computes disjoint term sets
representing topics in the collection. The algorithm relies on probabilities of
word co-occurrences to partition the set of terms appearing in the collection
of documents into disjoint groups of related terms. In this work, we also
present an environment to visualize the computed topics in the term space and
retrieve the most related PubMed articles for each group of terms. We
illustrate the algorithm by applying it to PubMed documents on the topic of
suicide. Suicide is a major public health problem identified as the tenth
leading cause of death in the US. In this application, our goal is to provide a
global view of the mental health literature pertaining to the subject of
suicide, and through this, to help create a rich environment of multifaceted
data to guide health care researchers in their endeavor to better understand
the breadth, depth and scope of the problem. We demonstrate the usefulness of
the proposed algorithm by providing a web portal that allows mental health
researchers to peruse the suicide-related literature in PubMed.
