9. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for selectively deleting clusters of conceptually-related words from a probabilistic generative model for textual documents, the method comprising: receiving a current model, which contains terminal nodes representing random variables for words and contains one or more cluster nodes representing clusters of conceptually related words; wherein nodes in the current model are coupled together by weighted links, so that for a cluster node with an incoming link from a node that has fired which causes the cluster node in the current model to fire with a probability proportionate to a weight of the incoming link, an outgoing link from the cluster node to another node causes the other node to fire with a probability proportionate to the weight of the outgoing link; and processing a given cluster node in the current model for possible deletion by, determining a number of outgoing links from the given cluster node to terminal nodes and/or cluster nodes in the current model; determining that the determined number of outgoing links is less than a minimum value; and deleting the given cluster node from the current model.
9. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for selectively deleting clusters of conceptually-related words from a probabilistic generative model for textual documents, the method comprising: receiving a current model, which contains terminal nodes representing random variables for words and contains one or more cluster nodes representing clusters of conceptually related words; wherein nodes in the current model are coupled together by weighted links, so that for a cluster node with an incoming link from a node that has fired which causes the cluster node in the current model to fire with a probability proportionate to a weight of the incoming link, an outgoing link from the cluster node to another node causes the other node to fire with a probability proportionate to the weight of the outgoing link; and processing a given cluster node in the current model for possible deletion by, determining a number of outgoing links from the given cluster node to terminal nodes and/or cluster nodes in the current model; determining that the determined number of outgoing links is less than a minimum value; and deleting the given cluster node from the current model. 13. The computer-readable storage medium of claim 9 , wherein the method further comprises performing training operations, which involves iteratively: receiving a set of training documents, wherein each training document contains a set of words; computing expected counts over the set of training documents; applying the expected counts to the current model to produce a new model, including the frequency with which cluster nodes are activated; and making the new model the current model.