Exploring new Approaches for Information Retrieval through Natural Language Processing
This is an incremental review paper summarizing existing approaches for researchers in IR and NLP.
This review paper examines recent advancements in Information Retrieval (IR) applied to Natural Language Processing (NLP), covering traditional models and modern techniques like deep learning and transformer models, and identifies open challenges for improving retrieval accuracy, scalability, and ethics.
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.