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Abstract

An unsupervised method of learning the relationships between words and unspecified topics in documents using a computer is described. The computer represents the relationships between words and unspecified topics via word clusters and association strength values, which can be used later during topical characterization of documents. The computer learns the relationships between words and unspecified topics in an iterative fashion from a set of learning documents. The computer preprocesses the training documents by generating an observed feature vector for each document of the set of training documents and by setting association strengths to initial values. The computer then determines how well the current association strength values predict the topical content of all of the learning documents by generating a cost for each document and summing the individual costs together to generate a total cost. If the total cost is excessive, the association strength values are modified and the total cost recalculated. The computer continues calculating total cost and modifying association strength values until a set of association strength values are discovered that adequately predict the topical content of the entire set of learning documents.

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