Indexing by Latent Dirichlet Allocation and an Ensemble Model

The contribution of this article is twofold. First, we present Indexing by latent Dirichlet allocation (LDI), an automatic document indexing method. Many ad hoc applications, or their variants with smoothing techniques suggested in LDA-based language modeling, can result in unsatisfactory performance as the document representations do not accurately reflect concept space. To improve document retrieval 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. EnM combines basic indexing models by assigning different weights and attempts to uncover the optimal weights to maximize the mean average precision. To solve the optimization problem, we propose an algorithm, 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.