Indexing by Latent Dirichlet Allocation and Ensemble Model

TL;DR

This paper introduces a new document indexing method using Latent Dirichlet Allocation and an ensemble retrieval model that optimizes weights for improved accuracy, validated on benchmark datasets.

Contribution

It presents a novel LDA-based indexing scheme and an ensemble model with boosting for enhanced document retrieval performance.

Findings

Both methods outperform baseline models on benchmark datasets.

The ensemble model with optimized weights achieves higher MAP scores.

LDA-based indexing provides more accurate concept representations.

Abstract

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.