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Deep Neural Networks for Query Expansion using Word Embeddings [article]

Ayyoob Imani, Amir Vakili, Ali Montazer, Azadeh Shakery
2018 arXiv   pre-print
We then introduce an artificial neural network classifier to predict the usefulness of query expansion terms. This classifier uses term word embeddings as inputs.  ...  In this paper, we show that this is also true for more recently proposed embedding-based query expansion methods.  ...  The neural network uses only pre-trained word embeddings and no manual feature selection or initial retrieval using the query is necessary.  ... 
arXiv:1811.03514v1 fatcat:nu2iw4u6frcepnvmsum4oahiwy

Getting Started with Neural Models for Semantic Matching in Web Search [article]

Kezban Dilek Onal, Ismail Sengor Altingovde, Pinar Karagoz, Maarten de Rijke
2016 arXiv   pre-print
This survey is meant as an introduction to the use of neural models for semantic matching. To remain focused we limit ourselves to web search.  ...  Recent advances in language technology have given rise to unsupervised neural models for learning representations of words as well as bigger textual units.  ...  A model that integrates a deep neural network for query classification and the DSSM model for web document ranking via shared layers is proposed.  ... 
arXiv:1611.03305v1 fatcat:agdgj7allbczxcyteuomswn574

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
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).  ...  A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing  ...  Finally, words remaining in the pruned tree are used for query expansion.  ... 
arXiv:1611.06792v3 fatcat:i2eqfj5l25epjcytgvifta4y4i

Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

Abhishek Kumar Shukla, Sujoy Das
2022 Computers Materials & Continua  
Word embedding has been applied by many researchers for Information retrieval tasks. In this paper word embedding-based skip-gram model has been developed for the query expansion task.  ...  Vocabulary terms are obtained from the top "k" initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for  ...  He expresses sincere thanks to the Institute for providing an opportunity for him to pursue his Ph.D. work.  ... 
doi:10.32604/cmc.2022.022411 fatcat:d3jjldoysjh3rpj7ailiyndh7e

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)  
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  ...  Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning.  ...  We would also like to thank our anonymous reviewers for their constructive comments and the guest editors for their advice.  ... 
doi:10.1007/s10791-017-9321-y fatcat:plrhhwkppjgb7l5r5daiyryj4q

Benchmark for Complex Answer Retrieval [article]

Federico Nanni, Bhaskar Mitra, Matt Magnusson, Laura Dietz
2017 arXiv   pre-print
., tf-idf) to complex systems that using query expansion using knowledge bases and deep neural networks.  ...  represented as vectors (e.g., word embedding vectors), and applications of deep neural networks and learning to rank.  ...  In the following, we cover three central ones, namely passage retrieval, query expansion using knowledge bases and the recent advancement in the use of deep neural network models for information retrieval  ... 
arXiv:1705.04803v1 fatcat:vgboyaprvzgvlevjs3xunlmhn4

Benchmark for Complex Answer Retrieval

Federico Nanni, Bhaskar Mitra, Matt Magnusson, Laura Dietz
2017 Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval - ICTIR '17  
., TF-IDF) to complex systems that adopt query expansion, knowledge bases and deep neural networks. e goal is to o er an overview of some promising approaches to tackle this problem.  ...  Acknowledgements e publication is funded in part through the scholarship of the Eliteprogramm for Postdocs of the Baden-Wür emberg Sti ung (project "Knowledge Consolidation and Organization for eryspeci  ...  represented as vectors (e.g., word embedding vectors), and applications of deep neural networks and learning to rank.  ... 
doi:10.1145/3121050.3121099 dblp:conf/ictir/NanniMMD17 fatcat:wtkbphutorcmvnhdz7mvtce6vq

GRAPHENE

Sendong Zhao, Chang Su, Andrea Sboner, Fei Wang
2019 Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19  
for each query.  ...  In this paper, we propose GRAPHENE, which is a deep learning based framework for precise BLR.  ...  Due to the limited number of clinical topics for training a deep neural IR model, we use the MeSH terms of each biomedical article as the corresponding query to pre-train deep neural models.  ... 
doi:10.1145/3357384.3358038 dblp:conf/cikm/ZhaoSSW19 fatcat:hpll7xork5em7jkoz4kz3m3gry

Relevance Feedback and Deep Neural Network-Based Semantic Method for Query Expansion

Abhishek Kumar Shukla, Sujoy Das, Pushpendra Kumar, Afroj Alam, Kuruva Lakshmanna
2022 Wireless Communications and Mobile Computing  
Document analysis and query expansion also use machine learning techniques at a broad scale for information retrieval tasks.  ...  The proposed method uses the relevance feedback method that selects a user-assisted most relevant document from top " k " initially retrieved documents and then applies deep neural network technique to  ...  He expresses sincere thanks to the Institute for providing an opportunity for him to pursue his Ph.D. work.  ... 
doi:10.1155/2022/6789044 fatcat:2k2kf2mhlzfwteoqevahpkhp6i

Using Word Embeddings for Automatic Query Expansion [article]

Dwaipayan Roy, Debjyoti Paul, Mandar Mitra, Utpal Garain
2016 arXiv   pre-print
In this paper a framework for Automatic Query Expansion (AQE) is proposed using distributed neural language model word2vec.  ...  Using semantic and contextual relation in a distributed and unsupervised framework, word2vec learns a low dimensional embedding for each vocabulary entry.  ...  INTRODUCTION In recent times, the IR and Neural Network (NN) communities have started to explore the application of deep neural network based techniques to various IR problems.  ... 
arXiv:1606.07608v1 fatcat:tsdo74tqzjfh5fvwpzmhiqtdqq

Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications

Vedran Vukotić, Christian Raymond, Guillaume Gravier
2016 Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval - ICMR '16  
Recently, deep neural networks, especially deep autoencoders, have proven promising both for crossmodal translation and for early fusion via multimodal embedding.  ...  In this work, we propose a flexible crossmodal deep neural network architecture for multimodal and crossmodal representation.  ...  In this work, we present a novel deep neural network architecture for crossmodal mapping and multimodal embedding.  ... 
doi:10.1145/2911996.2912064 dblp:conf/mir/VukoticRG16 fatcat:cqepgneporfrfhyosjiwkghqg4

Lecture Notes on Neural Information Retrieval [article]

Nicola Tonellotto
2022 arXiv   pre-print
These lecture notes focus on the recent advancements in neural information retrieval, with particular emphasis on the systems and models exploiting transformer networks.  ...  a basic understanding of the main information retrieval techniques and approaches based on deep learning.  ...  to a deep neural network, or it is implicitly generated and directly used by a deep neural network.  ... 
arXiv:2207.13443v1 fatcat:dessg6gmeveutf7cxh67w2fgeq

Multimodal and Crossmodal Representation Learning from Textual and Visual Features with Bidirectional Deep Neural Networks for Video Hyperlinking

Vedran Vukotić, Christian Raymond, Guillaume Gravier
2016 Proceedings of the 2016 ACM workshop on Vision and Language Integration Meets Multimedia Fusion - iV&L-MM '16  
BiDNN Crossmodal Query Expansion Bidirectional deep neural networks naturally enable crossmodal expansion where a missing modality is filled in by translating from the other one.  ...  In both cases it can be used to perform multimodal embedding and multimodal query expansion (filling of missing modalities with crossmodal translation) with a multitude of additional options.  ... 
doi:10.1145/2983563.2983567 dblp:conf/mm/VukoticRG16 fatcat:7esjh3ghczd5dnk55tn4hw4zxe

End-to-end Learning for Short Text Expansion [article]

Jian Tang, Yue Wang, Kai Zheng, Qiaozhu Mei
2017 arXiv   pre-print
Using short text classification as a demonstrating task, we show that the deep memory network significantly outperforms classical text expansion methods with comprehensive experiments on real world data  ...  The task is particularly challenging as a short text contains very sparse information, often too sparse for a machine learning algorithm to pick up useful signals.  ...  We thank the anonymous reviewers for their constructive comments. is work is supported by the National Institutes of Health under grant NLM 2R01LM010681-05 and the National Science Foundation under grant  ... 
arXiv:1709.00389v1 fatcat:4i6clfedfnaarbp3cbxlusp3vq

Neural Networks for Information Retrieval [article]

Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
2017 arXiv   pre-print
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.  ...  Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us.  ...  First, using pre-trained word embeddings like combining traditional retrieval models with an embedding-based translation model [16, 58] , using pre-trained embeddings for query expansion to improve retrieval  ... 
arXiv:1707.04242v1 fatcat:4idscmq26fa5bjupldwuyghq4m
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