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The need to organize a large collection in a manner that facilitates humancomprehension is crucial given the ever-increasing volumes of information. Inthis work, we present PDC (probabilistic distributional clustering), a novelalgorithm that, given a document collection, computes disjoint term setsrepresenting topics in the collection. The algorithm relies on probabilities ofword co-occurrences to partition the set of terms appearing in the collectionof documents into disjoint groups of related terms. In this work, we alsopresent an environment to visualize the computed topics in the term space andretrieve the most related PubMed articles for each group of terms. Weillustrate the algorithm by applying it to PubMed documents on the topic ofsuicide. Suicide is a major public health problem identified as the tenthleading cause of death in the US. In this application, our goal is to provide aglobal view of the mental health literature pertaining to the subject ofsuicide, and through this, to help create a rich environment of multifaceteddata to guide health care researchers in their endeavor to better understandthe breadth, depth and scope of the problem. We demonstrate the usefulness ofthe proposed algorithm by providing a web portal that allows mental healthresearchers to peruse the suicide-related literature in PubMed.

PDC -- a probabilistic distributional clustering algorithm: a case study on suicide articles in PubMed