In information retrieval, a fundamental goal is to transform a document into
concepts that are representative of its content. The term "representative" is
in itself challenging to define, and various tasks require different
granularities of concepts. In this paper, we aim to model concepts that are
sparse over the vocabulary, and that flexibly adapt their content based on
other relevant semantic information such as textual structure or associated
image features. We explore a Bayesian nonparametric model based on nested beta
processes that allows for inferring an unknown number of strictly sparse
concepts. The resulting model provides an inherently different representation
of concepts than a standard LDA (or HDP) based topic model, and allows for
direct incorporation of semantic features. We demonstrate the utility of this
representation on multilingual blog data and the Congressional Record.
Check out Figure 1 for the clear demonstration of their advancement.
http://arxiv.org/abs/1204.2523
