Neural information retrieval: at the end of the early years

By Kezban Dilek Onal, Ye Zhang, Ismail Sengör Altingövde, Md. Mustafizur Rahman, Pinar Senkul, Alexander Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara and 9 more. 2017

138 citations recorded by the source corpus.

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, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.

Subfields: Artificial Intelligence

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Citations in the corpus

Rethinking Search: Making Experts out of Dilettantes (2021, background): “For more information on this topic, interested readers should see [59] and [62].”
UvA-DARE (Digital Academic Repository) Learning to Transform, Combine, and Reason in Open-domain Question Answering (unknown): “Neural network based models [25, 27, 36, 43] are the most successful approaches in this area.”
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-Shot Learning (2019, methodology): “s have been primarily designed, trained, and evaluated on English corpora (e.g., [1,5,6,23]). More recently, a new wave of supervised state-of-the-art ranking models have been proposed by researchers [11,14,21,24,26,35,37]; these models rely on neural architectures to rerank the head of search resul”
A Survey of Model Architectures in Information Retrieval (2025, unknown): “C and detailed surveys (Onal et al., 2018; Mitra et al., 2018; Xu et al., 2018) independently encode queries and documents into a latent vector space, as illustrated in Fig.”