Patent ID: 7231393
Filing Date: 2007-06-12
Classification: G06F

Abstract:
1. A method for learning a generative model for text, comprising: receiving a current model, which contains terminal nodes representing random variables for words and can contain cluster nodes representing clusters of conceptually related words; wherein nodes in the current model are coupled together by weighted links, so that if an incoming link from a node that has fired causes a cluster node in the probabilistic model to fire with a probability proportionate to the 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, otherwise, the other node does not fire; receiving a set of training documents, wherein each training document contains a set of words; and applying the set of training documents to the current model to produce a new model, wherein applying the set of training documents to the current model involves computing for each cluster the probabilistic cost of the cluster existing in a document and triggering no words, and for each document applying this cost and subtracting the effects of words that do exist in the document.