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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).  ...  In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and  ...  In contrast to this work, this survey does not try to cover every aspect of neural IR, but will focus on and take a deep look into ranking models with deep neural networks.  ... 
arXiv:1903.06902v3 fatcat:j22ic7foibcurp45b4amdiwfhu

Neural Ranking Models for Document Retrieval [article]

Mohamed Trabelsi, Zhiyu Chen, Brian D. Davison, Jeff Heflin
2021 arXiv   pre-print
A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking.  ...  Recently, researchers have leveraged deep learning models in information retrieval.  ...  A regression framework for learning ranking functions using relative relevance judgments.  ... 
arXiv:2102.11903v1 fatcat:zc2otf456rc2hj6b6wpcaaslsa

Neural Networks for Information Retrieval [article]

Tom Kenter and Alexey Borisov and Christophe Van Gysel and Mostafa Dehghani and Maarten de Rijke and Bhaskar Mitra
2018 arXiv   pre-print
The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions.  ...  Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.  ...  Neural methods for ranking can use manually crafted query and document features, and combine them with regards to a ranking objective.  ... 
arXiv:1801.02178v1 fatcat:c3kevelcrffodift2vvwnoscjq

Neural Networks for Information Retrieval

Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions.  ...  .; Borisov, A.; Van Gysel, C.J.H.; Dehghani, M.; de Rijke, M.; Mitra, B. Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.  ...  information retrieval.  ... 
doi:10.1145/3077136.3082062 dblp:conf/sigir/KenterBGDRM17 fatcat:yxuiajzjlfaixlnhc6rrsud6ry

Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System

Rui Yan, Yiping Song, Hua Wu
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
In this paper, we propose a retrieval-based conversation system with the deep learning-torespond schema through a deep neural network framework driven by web data.  ...  The proposed model is general and unified for different conversation scenarios in open domain.  ...  DNN-based scoring, ranking, and ranked list fusion. We apply a deep neural network (DNN)-based model to rank optimization.  ... 
doi:10.1145/2911451.2911542 dblp:conf/sigir/YanSW16 fatcat:fnyvyxodczc6nczlvh5uzughja

Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, Haiqing Chen
2018 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18  
In this paper, we propose a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems.  ...  We incorporate external knowledge into deep neural models with pseudo-relevance feedback and QA correspondence knowledge distillation.  ...  knowledge into deep neural matching networks for response ranking.  ... 
doi:10.1145/3209978.3210011 dblp:conf/sigir/YangQQGZCHC18 fatcat:xt6767facjezpd6umj3ojrrgh4

Neural Networks for Information Retrieval [article]

Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
2017 arXiv   pre-print
The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions.  ...  Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.  ...  information retrieval.  ... 
arXiv:1707.04242v1 fatcat:4idscmq26fa5bjupldwuyghq4m

Neural Networks for Information Retrieval

Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
2018 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18  
The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions.  ...  .; Borisov, A.; Van Gysel, C.J.H.; Dehghani, M.; de Rijke, M.; Mitra, B. Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.  ...  information retrieval.  ... 
doi:10.1145/3159652.3162009 dblp:conf/wsdm/KenterBGDRM18 fatcat:ybdeuuxcbnh2np34k3y4ve5ovu

Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it) [article]

Christina Lioma and Birger Larsen and Casper Petersen and Jakob Grue Simonsen
2016 arXiv   pre-print
What if Information Retrieval (IR) systems did not just retrieve relevant information that is stored in their indices, but could also "understand" it and synthesise it into a single document?  ...  We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query.  ...  [21] "deep learned" both single and multiple term topics, which they integrated into a query likelihood language model for retrieval. Ranzato et al.  ... 
arXiv:1606.07660v2 fatcat:ymqxcsfwnvcsdfzneagojaneuq

A NeuRetrieval Model for Human-Computer Conversations

Rui Yan, Dongyan Zhao
2018 Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18  
In the experiments, we investigate the effectiveness of the proposed deep neural network model for human-computer conversations.  ...  Given a human issued utterance, i.e., a query, a retrieval-based conversation system will search for appropriate replies, conduct a relevance ranking, and then output the highly relevant one as the response  ...  ACKNOWLEDGMENTS We thank the reviewers for their insightful comments.  ... 
doi:10.1145/3184558.3186341 dblp:conf/www/YanZ18 fatcat:zkrrazyuavfsnkrx577njf5bku

A Social Search Model for Large Scale Social Networks [article]

Yunzhong He, Wenyuan Li, Liang-Wei Chen, Gabriel Forgues, Xunlong Gui, Sui Liang, Bo Hou
2020 arXiv   pre-print
In this paper, we present our solution to these two challenges by first introducing a social retrieval mechanism, and then investigate novel deep neural networks for the ranking problem.  ...  The result set is then ranked by a deep neural network that handles textual and social relevance in a two-tower approach, in which personalization and textual relevance are addressed jointly.  ...  Some recent research introduces social relations into information retrieval models by factoring them into user profiles and creating personalized re-ranking models [5, 11, 43, 47] which are often used  ... 
arXiv:2005.04356v1 fatcat:fvtxc3md2zegbfntzn3oubvyoi

Helping results assessment by adding explainable elements to the deep relevance matching model [article]

Ioannis Chios, Suzan Verberne
2021 arXiv   pre-print
We deduce the query term weights from the term gating network in the Deep Relevance Matching Model (DRMM) and visualize them as a doughnut chart.  ...  Thus, we conclude that the proposed explainable elements are promising as visualization for search engine users.  ...  To train the deep relevance matching model we employ a pairwise ranking loss function, which is widely used in neural ranking and in ad-hoc information retrieval in general, called hinge loss.  ... 
arXiv:2106.05147v1 fatcat:ob2f7tpugjharcrmfyqljx4w24

Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation [article]

Liu Yang, Hamed Zamani, Yongfeng Zhang, Jiafeng Guo, W. Bruce Croft
2017 arXiv   pre-print
Neural matching models, which adopt deep neural networks to learn sequence representations and matching scores, have attracted immense research interests of information retrieval and natural language processing  ...  In this paper, we first study neural matching models for the question retrieval task that has been widely explored in the literature, whereas the effectiveness of neural models for this task is relatively  ...  ACKNOWLEDGMENTS is work was supported in part by the Center for Intelligent Information Retrieval, in part by NSF IIS-1160894, and in part by NSF grant #IIS-1419693.  ... 
arXiv:1707.05409v1 fatcat:szimicqsijh2zb6lkizgi5zbxa

2020 Deep Learning Track

Tiago Almeida, Sérgio Matos
2020 Text Retrieval Conference  
We describe a two-stage retrieval pipeline for the TREC Deep Learning 2020 track, where we used a lightweight neural model to rerank a baseline produced by an efficient traditional technique.  ...  In terms of overall performance, our results are slightly below the median, with a best score of 0.  ...  Acknowledgments This work has received support from the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968 and from National Funds through the FCT -Foundation for  ... 
dblp:conf/trec/AlmeidaM20 fatcat:bj2m3uxaq5ftvfnkb66g5gylty

A Match-Transformer Framework for Modeling Diverse Relevance Patterns in Ad-hoc Retrieval

Yongyu Jiang, Peng Zhang, Hui Gao, Xindian Ma, Donghao Zhao, Zeting Hu, Junyan Wang, Meixian Song
2019 Australian Journal of Intelligent Information Processing Systems  
Meanwhile, we employ a special Convolution Network to capture effective matching patterns more subtle, and a Learning-to-Rank (L2R) algorithm to learn relative position information between documents.  ...  Experimental results on two benchmark collections demonstrate that our approach outperforms most wellknown Neural IR models in ad-hoc retrieval.  ...  Specially, deep neural networks have led to exciting breakthroughs on ad-hoc retrieval tasks.  ... 
dblp:journals/ajiips/JiangZGMZHWS19 fatcat:7istzbqbizgshjvvnasjbnbgpi
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