Abstract: One embodiment of the present invention provides a system that learns a generative model for textual documents. During operation, the system receives a current model, which contains terminal nodes representing random variables for words and cluster nodes representing clusters of conceptually related words. Within the current model, nodes are coupled together by weighted links, so that if a cluster node in the probabilistic model fires, a weighted link from the cluster node to another node causes the other node to fire with a probability proportionate to the link weight. The system also receives a set of training documents, wherein each training document contains a set of words. Next, the system applies the set of training documents to the current model to produce a new model.

Abstract: One embodiment of the present invention provides a system characterizes a document with respect to clusters of conceptually related words. Upon receiving a document containing a set of words, the system selects “candidate clusters” of conceptually related words that are related to the set of words. These candidate clusters are selected using a model that explains how sets of words are generated from clusters of conceptually related words. Next, the system constructs a set of components to characterize the document, wherein the set of components includes components for candidate clusters. Each component in the set of components indicates a degree to which a corresponding candidate cluster is related to the set of words.