Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing

TL;DR

This paper introduces a novel gradient search augmentation method using autoencoders for semantic hashing to enhance information retrieval speed and accuracy, demonstrating improved performance over traditional methods.

Contribution

It presents the gradient search augmentation (GSA) technique and evaluates its effectiveness alongside pseudo-relevance-feedback (PRF) in autoencoder-based semantic hashing for IR.

Findings

GSA improves TF-IDF retrieval performance

PRF combined with GSA yields the best results

Autoencoders create meaningful low-dimensional data encodings

Abstract

This paper will explore the use of autoencoders for semantic hashing in the context of Information Retrieval. This paper will summarize how to efficiently train an autoencoder in order to create meaningful and low-dimensional encodings of data. This paper will demonstrate how computing and storing the closest encodings to an input query can help speed up search time and improve the quality of our search results. The novel contributions of this paper involve using the representation of the data learned by an auto-encoder in order to augment our search query in various ways. I present and evaluate the new gradient search augmentation (GSA) approach, as well as the more well-known pseudo-relevance-feedback (PRF) adjustment. I find that GSA helps to improve the performance of the TF-IDF based information retrieval system, and PRF combined with GSA works best overall for the systems compared in this paper.