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.
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. 14. The computer-readable storage medium of claim 13 , wherein during an initial training iteration, the method further comprises generating an initial current model from a set of words by: generating a universal node that is always active; generating terminal nodes representing words in the set of words; generating cluster nodes by selecting training instances; and directly linking the universal node to the terminal nodes.