7. A method comprising: constructing queries from category descriptors representing categories of a taxonomy of hierarchically organized categories; querying a documents database using the constructed queries to retrieve pseudo-relevant documents; extracting category profiles for the categories of the taxonomy from at least the pseudo-relevant documents by inferring a hierarchical topic model representing the taxonomy wherein the hierarchical topic model comprises a hierarchical extension of the latent Dirichlet allocation (LDA) generative model and the inferring of a hierarchical LDA generative model representing the taxonomy comprises performing Gibbs sampling; and classifying an input document by optimizing mixture weights of a weighted combination of categories of the hierarchical topic model respective to the input document; wherein at least the constructing, extracting, and classifying operations are performed by a digital processing device.
7. A method comprising: constructing queries from category descriptors representing categories of a taxonomy of hierarchically organized categories; querying a documents database using the constructed queries to retrieve pseudo-relevant documents; extracting category profiles for the categories of the taxonomy from at least the pseudo-relevant documents by inferring a hierarchical topic model representing the taxonomy wherein the hierarchical topic model comprises a hierarchical extension of the latent Dirichlet allocation (LDA) generative model and the inferring of a hierarchical LDA generative model representing the taxonomy comprises performing Gibbs sampling; and classifying an input document by optimizing mixture weights of a weighted combination of categories of the hierarchical topic model respective to the input document; wherein at least the constructing, extracting, and classifying operations are performed by a digital processing device. 12. The method as set forth in claim 7 , wherein the hierarchical topic model comprises a mixture model in which a document is represented as a mixture of the language models of a plurality of categories and their ancestor categories.