Abstract

The present contribution suggests a two-step classification rule for unsupervised document classification, using one-class Support Vector Machines and Latent Dirichlet Allocation Topic Modeling. The integration of both algorithms allows the usage of labelled, but independent training data, not stemming from the data set to be classified. The manual labelling when trying to classify a specific class from an unlabelled data set can thus be circumvented. By choosing appropriate document representations and parameters in the one-class Support Vector Machine, the differences between the independent training class and the data set to be classified become negligible. The method is applied to a large data set on patents for the European Union.

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