A Review of Research in First-Stage Retrieval
暂无分享,去创建一个
Jie Yu | Shasha Li | Jun Ma | Huijun Liu | Mengxue Du | Miaomiao Li
[1] Hugo Proença,et al. Information Retrieval: Recent Advances and Beyond , 2023, IEEE Access.
[2] Jie Yu,et al. Topic-Grained Text Representation-based Model for Document Retrieval , 2022, ICANN.
[3] Huan-huan Zeng,et al. Learning to rank method combining multi-head self-attention with conditional generative adversarial nets , 2022, Array.
[4] Wayne Xin Zhao,et al. RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking , 2021, EMNLP.
[5] Danqi Chen,et al. Phrase Retrieval Learns Passage Retrieval, Too , 2021, EMNLP.
[6] Chenyan Xiong,et al. More Robust Dense Retrieval with Contrastive Dual Learning , 2021, ICTIR.
[7] Junchao Chen,et al. Construction of higher-order smooth positons and breather positons via Hirota’s bilinear method , 2021, Nonlinear Dynamics.
[8] Dani Yogatama,et al. End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering , 2021, NeurIPS.
[9] Fuzheng Zhang,et al. ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer , 2021, ACL.
[10] Danqi Chen,et al. SimCSE: Simple Contrastive Learning of Sentence Embeddings , 2021, EMNLP.
[11] Jiarun Cao,et al. Whitening Sentence Representations for Better Semantics and Faster Retrieval , 2021, ArXiv.
[12] Wonjong Rhee,et al. Improving Bi-encoder Document Ranking Models with Two Rankers and Multi-teacher Distillation , 2021, SIGIR.
[13] Jiafeng Guo,et al. Semantic Models for the First-Stage Retrieval: A Comprehensive Review , 2021, ACM Trans. Inf. Syst..
[14] Danqi Chen,et al. Learning Dense Representations of Phrases at Scale , 2020, ACL.
[15] Xinlei Chen,et al. Exploring Simple Siamese Representation Learning , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Jimmy J. Lin,et al. Distilling Dense Representations for Ranking using Tightly-Coupled Teachers , 2020, ArXiv.
[17] Hua Wu,et al. RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering , 2020, NAACL.
[18] Allan Hanbury,et al. Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation , 2020, ArXiv.
[19] Yinfei Yang,et al. Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation , 2020, ACL.
[20] Min Zhang,et al. RepBERT: Contextualized Text Embeddings for First-Stage Retrieval , 2020, ArXiv.
[21] Pierre H. Richemond,et al. Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning , 2020, NeurIPS.
[22] Fabio Petroni,et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , 2020, NeurIPS.
[23] Jacob Eisenstein,et al. Sparse, Dense, and Attentional Representations for Text Retrieval , 2020, Transactions of the Association for Computational Linguistics.
[24] Eugene Agichtein,et al. RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search , 2020, WWW.
[25] Danqi Chen,et al. Dense Passage Retrieval for Open-Domain Question Answering , 2020, EMNLP.
[26] Le Song,et al. DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding , 2020, SIGIR.
[27] 知秀 柴田. 5分で分かる!? 有名論文ナナメ読み:Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding , 2020 .
[28] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[29] Kevin Duh,et al. Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation , 2019, ArXiv.
[30] Ross B. Girshick,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Sung Ju Hwang,et al. Rethinking Data Augmentation: Self-Supervision and Self-Distillation , 2019, ArXiv.
[32] Jiashi Feng,et al. Revisit Knowledge Distillation: a Teacher-free Framework , 2019, ArXiv.
[33] Megha Nawhal,et al. Lifelong GAN: Continual Learning for Conditional Image Generation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[34] Ran El-Yaniv,et al. Multi-Hop Paragraph Retrieval for Open-Domain Question Answering , 2019, ACL.
[35] Nazli Goharian,et al. CEDR: Contextualized Embeddings for Document Ranking , 2019, SIGIR.
[36] W. Bruce Croft,et al. A Deep Look into Neural Ranking Models for Information Retrieval , 2019, Inf. Process. Manag..
[37] Seyed Iman Mirzadeh,et al. Improved Knowledge Distillation via Teacher Assistant , 2019, AAAI.
[38] Philip S. Yu,et al. Private Model Compression via Knowledge Distillation , 2018, AAAI.
[39] Yiqun Liu,et al. Unbiased Learning to Rank: Theory and Practice , 2018, ICTIR.
[40] Bo Li,et al. Joint Learning from Labeled and Unlabeled Data for Information Retrieval , 2018, COLING.
[41] Fernando Diaz,et al. SIGIR 2018 Workshop on Learning from Limited or Noisy Data for Information Retrieval , 2018, SIGIR.
[42] Md. Mustafizur Rahman,et al. Neural information retrieval: at the end of the early years , 2017, Information Retrieval Journal.
[43] Jin Young Choi,et al. Knowledge Distillation with Adversarial Samples Supporting Decision Boundary , 2018, AAAI.
[44] Bhaskar Mitra,et al. Cross Domain Regularization for Neural Ranking Models using Adversarial Learning , 2018, SIGIR.
[45] Ali Farhadi,et al. Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension , 2018, EMNLP.
[46] W. Bruce Croft,et al. Learning a Deep Listwise Context Model for Ranking Refinement , 2018, SIGIR.
[47] Jaap Kamps,et al. Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision , 2017, ArXiv.
[48] Xueqi Cheng,et al. DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval , 2017, CIKM.
[49] Miles Efron,et al. Document Expansion Using External Collections , 2017, SIGIR.
[50] Zhiyuan Liu,et al. End-to-End Neural Ad-hoc Ranking with Kernel Pooling , 2017, SIGIR.
[51] Huchuan Lu,et al. Deep Mutual Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[52] Jianfeng Gao,et al. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset , 2016, CoCo@NIPS.
[53] Nick Craswell,et al. Learning to Match using Local and Distributed Representations of Text for Web Search , 2016, WWW.
[54] W. Bruce Croft,et al. A Deep Relevance Matching Model for Ad-hoc Retrieval , 2016, CIKM.
[55] Azadeh Shakery,et al. Pseudo-Relevance Feedback Based on Matrix Factorization , 2016, CIKM.
[56] Ben He,et al. Training query filtering for semi-supervised learning to rank with pseudo labels , 2016, World Wide Web.
[57] Xueqi Cheng,et al. Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN , 2016, IJCAI.
[58] Bowen Zhou,et al. Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond , 2016, CoNLL.
[59] Xueqi Cheng,et al. A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations , 2015, AAAI.
[60] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[61] Alessandro Moschitti,et al. Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks , 2015, SIGIR.
[62] Xuanjing Huang,et al. Convolutional Neural Tensor Network Architecture for Community-Based Question Answering , 2015, IJCAI.
[63] Zhuo Wang,et al. Optimization and analysis of large scale data sorting algorithm based on Hadoop , 2015, ArXiv.
[64] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[65] Rabab Kreidieh Ward,et al. Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[66] Hang Li,et al. Convolutional Neural Network Architectures for Matching Natural Language Sentences , 2014, NIPS.
[67] Yelong Shen,et al. A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval , 2014, CIKM.
[68] Larry P. Heck,et al. Learning deep structured semantic models for web search using clickthrough data , 2013, CIKM.
[69] Zhendong Niu,et al. Concept Based Query Expansion , 2013, 2013 Ninth International Conference on Semantics, Knowledge and Grids.
[70] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[71] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .
[72] Jianfeng Gao,et al. Towards Concept-Based Translation Models Using Search Logs for Query Expansion , 2012, Proceedings of the 21st ACM international conference on Information and knowledge management.
[73] Katrina Fenlon,et al. Improving retrieval of short texts through document expansion , 2012, SIGIR '12.
[74] ChengXiang Zhai,et al. Axiomatic Analysis of Translation Language Model for Information Retrieval , 2012, ECIR.
[75] Jimmy J. Lin,et al. Pseudo test collections for learning web search ranking functions , 2011, SIGIR.
[76] Hang Li,et al. Book Reviews: Semantic Similarity from Natural Language and Ontology Analysis by Sébastien Harispe, Sylvie Ranwez, Stefan Janaqi, and Jacky Montmain , 2015, CL.
[77] Yue Lu,et al. Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA , 2011, Information Retrieval.
[78] Charles Elkan,et al. Latent semantic indexing (LSI) fails for TREC collections , 2011, SKDD.
[79] Hang Li,et al. Relevance Ranking Using Kernels , 2010, AIRS.
[80] Jianfeng Gao,et al. Clickthrough-based translation models for web search: from word models to phrase models , 2010, CIKM.
[81] Yi Liu,et al. Query Rewriting Using Monolingual Statistical Machine Translation , 2010, CL.
[82] Arantxa Otegi,et al. Document Expansion Based on WordNet for Robust IR , 2010, COLING.
[83] ChengXiang Zhai,et al. Estimation of statistical translation models based on mutual information for ad hoc information retrieval , 2010, SIGIR.
[84] Christopher D. Manning,et al. Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..
[85] ChengXiang Zhai,et al. A comparative study of methods for estimating query language models with pseudo feedback , 2009, CIKM.
[86] Tie-Yan Liu,et al. Ranking Measures and Loss Functions in Learning to Rank , 2009, NIPS.
[87] James Allan,et al. A Comparative Study of Utilizing Topic Models for Information Retrieval , 2009, ECIR.
[88] Tie-Yan Liu,et al. Learning to rank for information retrieval , 2009, SIGIR.
[89] W. Bruce Croft,et al. Search Engines - Information Retrieval in Practice , 2009 .
[90] Stephen E. Robertson,et al. Selecting good expansion terms for pseudo-relevance feedback , 2008, SIGIR '08.
[91] Quoc V. Le,et al. Learning to Rank with Nonsmooth Cost Functions , 2006, NIPS.
[92] W. Bruce Croft,et al. LDA-based document models for ad-hoc retrieval , 2006, SIGIR.
[93] Tao Tao,et al. Language Model Information Retrieval with Document Expansion , 2006, NAACL.
[94] Fernando Diaz,et al. Regularizing ad hoc retrieval scores , 2005, CIKM '05.
[95] Gregory N. Hullender,et al. Learning to rank using gradient descent , 2005, ICML.
[96] W. Bruce Croft,et al. Cluster-based retrieval using language models , 2004, SIGIR '04.
[97] Jianfeng Gao,et al. Dependence language model for information retrieval , 2004, SIGIR '04.
[98] Oren Kurland,et al. Corpus structure, language models, and ad hoc information retrieval , 2004, SIGIR '04.
[99] James Allan,et al. Capturing term dependencies using a language model based on sentence trees , 2002, CIKM '02.
[100] C. J. van Rijsbergen,et al. Probabilistic models of information retrieval based on measuring the divergence from randomness , 2002, TOIS.
[101] John D. Lafferty,et al. Model-based feedback in the language modeling approach to information retrieval , 2001, CIKM '01.
[102] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[103] Stephen E. Robertson,et al. A probabilistic model of information retrieval: development and comparative experiments - Part 2 , 2000, Inf. Process. Manag..
[104] W. Bruce Croft,et al. A general language model for information retrieval , 1999, CIKM '99.
[105] John D. Lafferty,et al. Information retrieval as statistical translation , 1999, SIGIR '99.
[106] W. Bruce Croft,et al. A Language Modeling Approach to Information Retrieval , 1998, SIGIR Forum.
[107] Claire Cardie,et al. An Analysis of Statistical and Syntactic Phrases , 1997, RIAO.
[108] Ellen M. Voorhees,et al. Query expansion using lexical-semantic relations , 1994, SIGIR '94.
[109] G Salton,et al. Developments in Automatic Text Retrieval , 1991, Science.
[110] Richard A. Harshman,et al. Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..
[111] Gerard Salton,et al. Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..
[112] Joel L Fagan,et al. Experiments in Automatic Phrase Indexing For Document Retrieval: A Comparison of Syntactic and Non-Syntactic Methods , 1987 .
[113] P. C. Wong,et al. Generalized vector spaces model in information retrieval , 1985, SIGIR '85.
[114] Van Rijsbergen,et al. A theoretical basis for the use of co-occurence data in information retrieval , 1977 .
[115] Stephen E. Robertson,et al. Relevance weighting of search terms , 1976, J. Am. Soc. Inf. Sci..
[116] Gerard Salton,et al. A vector space model for automatic indexing , 1975, CACM.
[117] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[118] Yoav Goldberg. Neural Network Methods for Natural Language Processing , 2017 .
[119] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[120] Justin Zobel,et al. Document expansion versus query expansion for ad-hoc retrieval , 2005 .
[121] Yuet Meng. Lee,et al. Query expansion using lexical-semantic relations , 1999 .
[122] J. J. Rocchio,et al. Relevance feedback in information retrieval , 1971 .
[123] Gerard Salton,et al. The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .
[124] Michael Lesk,et al. Word-word associations in document retrieval systems , 1969 .