Exploring new Approaches for Information Retrieval through Natural Language Processing

TL;DR

This review surveys recent advancements in Information Retrieval within NLP, covering traditional models, modern deep learning techniques, tools, applications, and future challenges to improve retrieval systems.

Contribution

It provides a comprehensive overview of both classical and cutting-edge IR approaches, including a comparative analysis and discussion of open research challenges.

Findings

Deep learning and transformer models enhance IR performance.

Hybrid retrieval methods balance efficiency and accuracy.

Open challenges include scalability and ethical considerations.

Abstract

This review paper explores recent advancements and emerging approaches in Information Retrieval (IR) applied to Natural Language Processing (NLP). We examine traditional IR models such as Boolean, vector space, probabilistic, and inference network models, and highlight modern techniques including deep learning, reinforcement learning, and pretrained transformer models like BERT. We discuss key tools and libraries - Lucene, Anserini, and Pyserini - for efficient text indexing and search. A comparative analysis of sparse, dense, and hybrid retrieval methods is presented, along with applications in web search engines, cross-language IR, argument mining, private information retrieval, and hate speech detection. Finally, we identify open challenges and future research directions to enhance retrieval accuracy, scalability, and ethical considerations.