Unsupervised learning and the information retrieval problem

The author presents two implemented neuronal methods for free-text database search in details. In the first method, a specific interest (or query) is taught to a Kohonen feature map. By using this network as a neural filter on a dynamic free-text database, only the associated subjects are selected from this database. The second method can be used in a more static environments. Statistical properties (n-grams) from various texts are taught to a feature map. A comparison of a query with this feature map results in the selection of texts with are closely related with respect to their contents. Both methods are compared with classical statistical information-retrieval algorithms. Various simulations show that the neural net converges towards a proper representation of the query as well as the objects in the database. The first algorithm exhibits much better scalability than its statistical counterparts, resulting in higher speeds, less memory needs, and easier maintainability. The second one shows an elegant and uniform generalization and association method, increasing the selection quality