17. The process of claim 1 , wherein the process action of identifying clusters of keyword candidates in each of the groups of keyword candidates, comprises the action of: constructing a co-occurrence graph in the form of a weighted undirected graph comprising vertices and edges, wherein each vertex represents a different one of the candidate keywords and each edge connects a pair of vertices whose corresponding candidate keywords co-occur in at least one of the documents in the set of related documents, and wherein a weight is assigned to each edge which represents the number of documents in which the candidate keywords associated with the connected vertices co-occur; discarding each edge whose assigned weight does not exceed a prescribed minimum edge weight threshold; eliminating the candidate keyword associated with each vertex, if any, that is not connected to at least one other vertex by an edge, from consideration as a related keyword; designating each group of interconnected vertices as a sub-graph; for each sub-graph, computing the density of the sub-graph, determining if the sub-graph density exceeds a prescribed density threshold, and designating the sub-graph as a dense cluster of keyword candidates whenever its density exceeds the prescribed density threshold.
1. A computer-implemented process for extracting topically related keywords from topically related documents, comprising: using a computer to perform the following process actions: accessing a set of topically related documents; identifying a number of candidate keywords from the set of related documents, wherein a candidate keyword can be an individual term or a multiple word phrase; forming a weighted keyword candidate-document matrix using the candidate keywords; partitioning the keyword candidate-document matrix into multiple groups of keyword candidates; identifying dense clusters of keyword candidates in each of the groups of keyword candidates whose density exceeds a prescribed density threshold, said keyword candidate cluster density for a group of keyword candidates being based on co-occurrences of keyword candidates belonging to the group in documents from the set of related documents; and for each of the identified dense clusters, designating the keyword candidates associated with that cluster as topically related keywords, and extracting the topically related keywords from the set of topically related documents.