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Neural Information Retrieval models hold the promise to replace lexical matching models, e.g.
Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. Journal of the American Society for Information Science 27
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Yu, C.T., Salton, G.: Precision Weighting - An Effective Automatic Indexing Method. Journal of the ACM 23
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Robertson, S.E.: The Probability Ranking Principle in IR. Journal of Documentation 33
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Robertson, S.E., Zaragoza, H.: The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval (2009)
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2011
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2016
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Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423, https://doi.org/10.18653/v1/n19-1423
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Camara, A., Hauff, C.: Diagnosing BERT with Retrieval Heuristics. In: ECIR. p. 14 (2020), zSCC: NoCitationData[s0]
2020
Cited alongside, same era.
Craswell, N., Mitra, B., Yilmaz, E., Campos, D., Voorhees, E.M.: Overview of the trec 2019 deep learning track (2020)
2020
Cited alongside, same era.
Craswell, N., Mitra, B., Yilmaz, E., Campos, D.: Overview of the trec 2020 deep learning track (2021)
2021
Formal, T., Piwowarski, B., Clinchant, S.: A White Box Analysis of ColBERT. In: Hiemstra, D., Moens, M.F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) Advances in Information Retrieval. pp. 257–263. Lecture Notes in Computer Science, Springer International Publishing, Cham (2021). https://doi.org/10/gjn2cd
2021
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Hofstätter, S., Lin, S.C., Yang, J.H., Lin, J., Hanbury, A.: Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling. In: SIGIR (Jul 2021)
2021
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Jiang, Z., Tang, R., Xin, J., Lin, J.: How Does BERT Rerank Passages? An Attribution Analysis with Information Bottlenecks. In: EMNLP Workshop, Black Box NLP. p. 14 (2021)
2021
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Sciavolino, C., Zhong, Z., Lee, J., Chen, D.: Simple entity-centric questions challenge dense retrievers. In: Empirical Methods in Natural Language Processing (EMNLP) (2021)
2021
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Cited alongside, same era.
2021
Cited alongside, same era.
Formal, T., Piwowarski, B., Clinchant, S.: Splade: Sparse lexical and expansion model for first stage ranking. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. p. 2288–2292. SIGIR ’21, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3404835.3463098, https://doi.org/10.1145/3404835.3463098
2021
Cited alongside, same era.
Nogueira, R., Lin, J.: From doc2query to docTTTTTquery p. 3, zSCC: 0000004
Cited in the paper.
2021
Closest in time.
Yates, A., Nogueira, R., Lin, J.: Pretrained transformers for text ranking: Bert and beyond. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining. p. 1154–1156. WSDM ’21, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3437963.3441667, https://doi.org/10.1145/3437963.3441667
2021
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