DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval

@article{Pang2017DeepRankAN,
  title={DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval},
  author={Liang Pang and Yanyan Lan and J. Guo and Jun Xu and Jingfang Xu and Xueqi Cheng},
  journal={Proceedings of the 2017 ACM on Conference on Information and Knowledge Management},
  year={2017}
}
  • Liang Pang, Yanyan Lan, Xueqi Cheng
  • Published 16 October 2017
  • Computer Science
  • Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
This paper concerns a deep learning approach to relevance ranking in information retrieval (IR. [] Key Method Firstly, a detection strategy is designed to extract the relevant contexts. Then, a measure network is applied to determine the local relevances by utilizing a convolutional neural network (CNN) or two-dimensional gated recurrent units (2D-GRU). Finally, an aggregation network with sequential integration and term gating mechanism is used to produce a global relevance score. DeepRank well captures…

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