  The contribution of this paper is two-fold. First, we present Indexing by
Latent Dirichlet Allocation (LDI), an automatic document indexing method. The
probability distributions in LDI utilize those in Latent Dirichlet Allocation
(LDA), a generative topic model that has been previously used in applications
for document retrieval tasks. However, the ad hoc applications, or their
variants with smoothing techniques as prompted by previous studies in LDA-based
language modeling, result in unsatisfactory performance as the document
representations do not accurately reflect concept space. To improve
performance, we introduce a new definition of document probability vectors in
the context of LDA and present a novel scheme for automatic document indexing
based on LDA. Second, we propose an Ensemble Model (EnM) for document
retrieval. The EnM combines basis indexing models by assigning different
weights and attempts to uncover the optimal weights to maximize the Mean
Average Precision (MAP). To solve the optimization problem, we propose an
algorithm, EnM.B, which is derived based on the boosting method. The results of
our computational experiments on benchmark data sets indicate that both the
proposed approaches are viable options for document retrieval.
