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Neural information retrieval: at the end of the early years

Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner (+7 others)
2017 Information retrieval (Boston)  
Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created.  ...  A recent "third wave" of neural network (NN) approaches now delivers state-ofthe-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing  ...  The following additional students at the University of Texas at Austin contributed indirectly to the writing of this literature review: Manu Agarwal, Edward Babbe, Anuparna Banerjee, Jason Cai, Dillon  ... 
doi:10.1007/s10791-017-9321-y fatcat:plrhhwkppjgb7l5r5daiyryj4q

Neural information retrieval: introduction to the special issue

Nick Craswell, W. Bruce Croft, Maarten de Rijke, Jiafeng Guo, Bhaskar Mitra
2017 Information retrieval (Boston)  
Acknowledgements We thank Charles Clarke, co-editor of the journal, for his guidance during the editing of this special issue.  ...  We further like to thank all the authors who submitted their work and the reviewers who spent time and effort providing invaluable feedback in the making of this special issue.  ...  This special issue of the Information Retrieval journal provides an additional venue for the findings from research happening at the intersection of information retrieval and neural networks.  ... 
doi:10.1007/s10791-017-9323-9 fatcat:xzl5wtc67bgwhiru2prdw4k6oe

Neural Information Retrieval: A Literature Review [article]

Ye Zhang, Md Mustafizur Rahman, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek Khetan, Tyler McDonnell, An Thanh Nguyen (+3 others)
2017 arXiv   pre-print
Stemming from this tide of NN work, a number of researchers have recently begun to investigate NN approaches to Information Retrieval (IR).  ...  In this work, we survey the current landscape of Neural IR research, paying special attention to the use of learned representations of queries and documents (i.e., neural embeddings).  ...  Additional Authors The following additional students at the University of Texas at Austin contributed indirectly to the writing of this literature review: Manu Agarwal, Edward Babbe, Anuparna Banerjee,  ... 
arXiv:1611.06792v3 fatcat:i2eqfj5l25epjcytgvifta4y4i

ReNeuIR: Reaching Efficiency in Neural Information Retrieval

Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
We propose this workshop as a forum for a critical discussion of efficiency in the era of neural information retrieval, to encourage debate on the current state and future directions of research in this  ...  space, and to promote more sustainable research by identifying best practices in the development and evaluation of neural models for information retrieval.  ...  He won the Best Paper Award at the ACM SIGIR Conference on Research and Development in Information Retrieval 2015.  ... 
doi:10.1145/3477495.3531704 fatcat:3kh2ykbt7zgejoavha2vtwolsq

DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval [article]

Zhiwen Tang, Grace Hui Yang
2018 arXiv   pre-print
Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching.  ...  Although its design and implementation are light-weight, DeepTileBars outperforms other state-of-the-art Neu-IR models on benchmark datasets including the Text REtrieval Conference (TREC) 2010-2012 Web  ...  Any opinions, findings, conclusions, or recommendations expressed in this paper are of the authors, and do not necessarily reflect those of the sponsor.  ... 
arXiv:1811.00606v2 fatcat:xgvq7gybtramhkjru5dusshshq

Neural Vector Spaces for Unsupervised Information Retrieval

Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
2018 ACM Transactions on Information Systems  
We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval.  ...  The addition of NVSM to a mixture of lexical language models and a state-of-the-art baseline vector space model yields a statistically significant increase in retrieval effectiveness.  ...  ACKNOWLEDGMENTS We thank Adith Swaminathan, Alexey Borisov, Tom Kenter, Hosein Azarbonyad, Mostafa Dehghani, Nikos Voskarides and Adam Holmes and the anonymous reviewers for their helpful comments.  ... 
doi:10.1145/3196826 fatcat:46qldllsnfd4xfyrrvw7xrtjhq

Parameterized Neural Network Language Models for Information Retrieval [article]

Benjamin Piwowarski and Sylvain Lamprier and Nicolas Despres
2015 arXiv   pre-print
Information Retrieval (IR) models need to deal with two difficult issues, vocabulary mismatch and term dependencies.  ...  In parallel, in the last few years, language models based on neural networks have been used to cope with complex natural language processing tasks like emotion and paraphrase detection.  ...  We apply the model for ad-hoc Information Retrieval; 3 . We perform intensive experiments on standard IR collections (TREC-1 to 8) and analyze the results. The outline of the paper is as follows.  ... 
arXiv:1510.01562v1 fatcat:lwhi6aqbjjezni3lwhowhp3y5e

A Deep Look into Neural Ranking Models for Information Retrieval [article]

Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, Xueqi Cheng
2019 arXiv   pre-print
Ranking models lie at the heart of research on information retrieval (IR).  ...  The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features.  ...  Learning to rank models are often employed to model the last stage ranker whose goal is to re-rank a small set of documents retrieved by the early stage rankers.  ... 
arXiv:1903.06902v3 fatcat:j22ic7foibcurp45b4amdiwfhu

Continual Learning of Long Topic Sequences in Neural Information Retrieval [article]

Thomas Gerald, Laure Soulier
2022 arXiv   pre-print
In information retrieval (IR) systems, trends and users' interests may change over time, altering either the distribution of requests or contents to be recommended.  ...  We then in-depth analyze the ability of recent neural IR models while continually learning those streams.  ...  We thank the ANR JCJC SESAMS project (ANR-18-CE23-0001) for supporting this work. This work was performed using HPC resources from GENCI-IDRIS (Grant 2021-101681).  ... 
arXiv:2201.03356v1 fatcat:mowiqrb4f5dafam6o6vicaemzm

Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits [article]

Leonid Boytsov, Zico Kolter
2021 arXiv   pre-print
We study the utility of the lexical translation model (IBM Model 1) for English text retrieval, in particular, its neural variants that are trained end-to-end.  ...  on the maximum sequence length of existing BERT models.  ...  Neural Ranking models have been a popular topic in recent years [24] , but the success of early approaches-which predate BERT-was controversial [39] .  ... 
arXiv:2102.06815v2 fatcat:bg74b25ks5e4lk7za25j6s6ace

Report on the Second SIGIR Workshop on Neural Information Retrieval (Neu-IR'17)

Nick Craswell, W. Bruce Croft, Maarten de Rijke, Jiafeng Guo, Bhaskar Mitra
2018 SIGIR Forum  
The second SIGIR workshop on neural information retrieval (Neu-IR'17) took place on August 11, 2017, in Tokyo, Japan.  ...  Organizers of four of the TREC 2017 tracks were invited to present at the workshop on how these IR tasks may be suitable for evaluating recent data-hungry neural approaches.  ...  Introduction Following the popularity of the 2016 edition [1, 2] , the Neu-IR workshop on neural information retrieval (IR) returned to SIGIR 2017 in Tokyo, Japan.  ... 
doi:10.1145/3190580.3190603 fatcat:23pwl6jtnbdh3ifxexlwqozdhu

CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching

Zeping Yu, Wenxin Zheng, Jiaqi Wang, Qiyi Tang, Sen Nie, Shi Wu
2020 Neural Information Processing Systems  
This paper proposes an end-to-end cross-modal retrieval network for binary source code matching, which achieves higher accuracy and requires less expert experience.  ...  Binary source code matching, especially on function-level, has a critical role in the field of computer security.  ...  With the development of deep learning in recent years, a general framework for cross-modal retrieval tends to be popular.  ... 
dblp:conf/nips/YuZW0NW20 fatcat:pfrshpubvnh7jmmgkkdvecwa4q

Neural Factorization Machines for Sparse Predictive Analytics

Xiangnan He, Tat-Seng Chua
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
NFM seamlessly combines the linearity of FM in modelling second-order feature interactions and the non-linearity of neural network in modelling higher-order feature interactions.  ...  While deep neural networks have recently been applied to learn non-linear feature interactions in industry, such as the Wide&Deep by Google and DeepCross by Microso , the deep structure meanwhile makes  ...  INTRODUCTION Predictive analytics is one of the most important techniques for many information retrieval (IR) and data mining (DM) tasks, ranging from recommendation systems [2, 16] , targeted advertising  ... 
doi:10.1145/3077136.3080777 dblp:conf/sigir/0001C17 fatcat:edqrsrngffb4hi6cqnktd4j33a

An Encrypted Speech Retrieval Scheme Based on Long Short-Term Memory Neural Network and Deep Hashing

2020 KSII Transactions on Internet and Information Systems  
Due to the explosive growth of multimedia speech data, how to protect the privacy of speech data and how to efficiently retrieve speech data have become a hot spot for researchers in recent years.  ...  This scheme not only achieves efficient retrieval of massive speech in cloud environment, but also effectively avoids the risk of sensitive information leakage.  ...  The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.  ... 
doi:10.3837/tiis.2020.06.016 fatcat:alfme7ukibd3tjgxswt3xvprxe

Convolutional neural networks and hash learning for feature extraction and of fast retrieval of pulmonary nodules

Pinle Qin, Jun Chen, Kai Zhang, Rui Chai
2018 Computer Science and Information Systems  
Using deep convolution neural network (CNN) to construct the CBMIR system can fully characterize the high level semantic features information for medical image retrieval.  ...  This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems.  ...  Recently, with the advent of deep learning for hashing, we are able to perform effective end-to-end learning of binary representations directly from input images.  ... 
doi:10.2298/csis171210020q fatcat:t7hons6mjfevncwqaomsx3pqxq
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