Neural information retrieval: at the end of the early years

作者：Onal Kezban Dilek; Zhang Ye; Altingovde Ismail Sengor; Rahman Md Mustafizur; Karagoz Pinar; Braylan Alex; Dang Brandon; Chang Heng-Lu; Kim Henna; McNamara Quinten; Angert Aaron; Banners Edward; Khetan Vivek; McDonnell Tyler; An Thanh Nguyen; Xu Dan; Wallace Byron C.; de Rijke Maarten; Lease Matthew

来源：Information Retrieval Journal, 2018, 21(2-3): 111-182.

DOI：10.1007/s10791-017-9321-y

• Deep learning
• Distributed representation
• Neural network
• Recurrent neural network
• Search engine
• Word embedding
• Semantic matching
• Semantic compositionality

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.

• 出版日期2018-6
• 单位IBM