Proceedings ArticleDOI
Deep Learning for Information Retrieval
Hang Li,Zhengdong Lu +1 more
- pp 1203-1206
TLDR
This tutorial aims at summarizing and introducing the results of recent research on deep learning for information retrieval, in order to stimulate and foster more significant research and development work on the topic in the future.Abstract:
Recent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully applied into almost all of their major tasks. The key to the success of deep learning is its capability of accurately learning distributed representations (vector representations or structured arrangement of them) of natural language expressions such as sentences, and effectively utilizing the representations in the tasks. This tutorial aims at summarizing and introducing the results of recent research on deep learning for information retrieval, in order to stimulate and foster more significant research and development work on the topic in the future. The tutorial mainly consists of three parts. In the first part, we introduce the fundamental techniques of deep learning for natural language processing and information retrieval, such as word embedding, recurrent neural networks, and convolutional neural networks. In the second part, we explain how deep learning, particularly representation learning techniques, can be utilized in fundamental NLP and IR problems, including matching, translation, classification, and structured prediction. In the third part, we describe how deep learning can be used in specific application tasks in details. The tasks are search, question answering (from either documents, database, or knowledge base), and image retrieval.read more
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Neural Models for Information Retrieval
Bhaskar Mitra,Nick Craswell +1 more
TL;DR: This tutorial introduces basic concepts and intuitions behind neural IR models, and places them in the context of traditional retrieval models, by introducing fundamental concepts of IR and different neural and non-neural approaches to learning vector representations of text.
Journal ArticleDOI
Neural information retrieval: at the end of the early years
Kezban Dilek Onal,Kezban Dilek Onal,Ye Zhang,Ismail Sengor Altingovde,Md. Mustafizur Rahman,Pinar Karagoz,Alexander Braylan,Brandon Dang,Heng-Lu Chang,Henna Kim,Quinten McNamara,Aaron Angert,Edward Banner,Vivek Khetan,Tyler McDonnell,An Thanh Nguyen,Dan Xu,Byron C. Wallace,Maarten de Rijke,Matthew Lease +19 more
TL;DR: The successes of neural IR thus far are highlighted, obstacles to its wider adoption are cataloged, and potentially promising directions for future research are suggested.
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Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
Jun Wang,Weinan Zhang,Shuai Yuan +2 more
TL;DR: Topics covered include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimization, statistical arbitrage, dynamic pricing, and ad fraud detection are an invaluable text for researchers and practitioners alike.
Journal ArticleDOI
Best Match: New relevance search for PubMed.
Nicolas Fiorini,Kathi Canese,Grisha Starchenko,Evgeny Kireev,Won Kim,Vadim Miller,Maxim Osipov,Michael Kholodov,Rafis Ismagilov,Sunil Mohan,James Ostell,Zhiyong Lu +11 more
TL;DR: This work presents Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of users and cutting-edge machine-learning technology as an alternative to the traditional date sort order.
Journal ArticleDOI
Adversarial Transfer Learning for Deep Learning Based Automatic Modulation Classification
TL;DR: An adversarial transfer learning architecture (ATLA), incorporating adversarial training and knowledge transfer in a unified way, is proposed, which substantially boosts the performance of the target model, which outperforms the existing parameter-transfer approach.
References
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Journal ArticleDOI
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Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
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Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.