Neural Information Retrieval: A Literature Review

Abstract

A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, this new NN research is often referred to as deep learning. Stemming from this tide of NN work, a number of researchers have recently begun to investigate NN approaches to Information Retrieval (IR). While deep NNs have yet to achieve the same level of success in IR as seen in other areas, the recent surge of interest and work in NNs for IR suggest that this state of affairs may be quickly changing. In this work, we survey the current landscape of Neural IR research, paying special attention to the use of learned representations of queries and documents (i.e., neural embeddings). We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directi

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyzhang2016neural

Related papers

• Neural Models For Information Retrieval (2017)0.00
• A Deep Look Into Neural Ranking Models For Information Retrieval (2019)17.73
• SEINE: Segment-based Indexing For Neural Information Retrieval (2023)0.00
• Incorporating Query Term Independence Assumption For Efficient Retrieval And Ranking Using Deep Neural Networks (2019)0.00
• Neural Ranking Models For Document Retrieval (2021)11.08
• Beyond Precision: A Study On Recall Of Initial Retrieval With Neural Representations (2018)4.52
• Neural IR Meets Graph Embedding: A Ranking Model For Product Search (2019)11.85
• A Few Brief Notes On Deepimpact, COIL, And A Conceptual Framework For Information Retrieval Techniques (2021)0.00