research-article

Neural ranking models for document retrieval

Authors:

Mohamed Trabelsi,

Brian D. Davison,

Jeff HeflinAuthors Info & Claims

Information Retrieval Journal, Volume 24, Issue 6

Pages 400 - 444

https://doi.org/10.1007/s10791-021-09398-0

Published: 01 December 2021 Publication History

Abstract

Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.

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Index Terms

Neural ranking models for document retrieval
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Index terms have been assigned to the content through auto-classification.

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cover image Information Retrieval

Information Retrieval Volume 24, Issue 6

Dec 2021

74 pages

ISSN:1386-4564

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Published: 01 December 2021

Accepted: 02 October 2021

Received: 08 February 2021

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