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Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations.
Distributed Representations of Words and Phrases and their Compositionality. In NIPS
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
A Deep Relevance Matching Model for Ad-hoc Retrieval. In CIKM
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016 · 2016
Earlier work this paper cites.
Neural Ranking Models with Weak Supervision. In SIGIR
Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, and W. Bruce Croft. 2017 · 2017
Earlier work this paper cites.
DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval. In CIKM
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Jingfang Xu, and Xueqi Cheng. 2017 · 2017
Cited alongside, same era.
End-to-End Neural Ad-hoc Ranking with Kernel Pooling. In SIGIR
Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017 · 2017
Cited alongside, same era.
Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search. In WSDM
Zhuyun Dai, Chenyan Xiong, Jamie Callan, and Zhiyuan Liu. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations. In NAACL
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 2019
Closest in time.
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