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Classical information retrieval (IR) methods, such as query likelihood and BM25, score documents independently w.r.t.
A language modeling approach to information retrieval
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Simple bm25 extension to multiple weighted fields
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End-to-end neural ad-hoc ranking with kernel pooling
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Off the beaten path: Let’s replace term-based retrieval with k-nn search
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A deep relevance matching model for ad-hoc retrieval
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft · 2016
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Improving document ranking with dual word embeddings
Eric Nalisnick, Bhaskar Mitra, Nick Craswell, and Rich Caruana · 2016
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Text matching as image recognition
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, and Xueqi Cheng · 2016
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From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing
Hamed Zamani, Mostafa Dehghani, W Bruce Croft, Erik Learned-Miller, and Jaap Kamps
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Neural ranking models with multiple document fields
Hamed Zamani, Bhaskar Mitra, Xia Song, Nick Craswell, and Saurabh Tiwary
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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An introduction to neural information retrieval
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An updated duet model for passage re-ranking
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