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Probabilistic Models for Hierarchical Clustering and Categorisation : Applications in the information Society

Eric Gaussier, Cyril Goutte
We propose a new hierarchical generative model for textual data, where words may be generative model for textual data, where words may be generated by topic specific distributions at any level in the hierarchy. This model is naturally well-suited to clustering documents in preset or automatically generated hierarchies , as well as categorising new documents in an existing hierarchy. Furthermore, we present a series of applications that can benefit from our model, as well as an experimental evaluation for both clustering and categorisation on frequently used data sets.
Proceedings of the Intl. Conf. on Advances in Infrastructure for Electronic Business, Education, Science and Medicine on the Internet, L'Aquila,Italy, January 21-27 2002.

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