Neural information retrieval: introduction to the special issue

The applications of neural network models, shallow or deep, to information retrieval (IR) tasks falls under the purview of neural IR. Over the years, machine learning methods-including neural networks-have been popularly employed in IR, such as in learning-to-rank (LTR) frameworks. Recently, neural representation learning and neural models with deep architectures have demonstrated significant improvements in speech recognition, machine translation, and computer vision tasks. Similar methods are now being explored by the IR community that may lead to new models and performance breakthroughs for retrieval scenarios. 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.

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[15]  Lin Ma,et al.  Multimodal Convolutional Neural Networks for Matching Image and Sentence , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[16]  Bhaskar Mitra,et al.  Query Auto-Completion for Rare Prefixes , 2015, CIKM.

[17]  Marie-Francine Moens,et al.  Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings , 2015, SIGIR.

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[20]  Bhaskar Mitra,et al.  Benchmark for Complex Answer Retrieval , 2017, ICTIR.

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[22]  Bhaskar Mitra,et al.  Reply With: Proactive Recommendation of Email Attachments , 2017, CIKM.

[23]  Bhaskar Mitra,et al.  An Introduction to Neural Information Retrieval , 2018, Found. Trends Inf. Retr..

[24]  Xuan Liu,et al.  Multi-view Response Selection for Human-Computer Conversation , 2016, EMNLP.

[25]  Zhengdong Lu,et al.  Deep Learning for Information Retrieval , 2016, SIGIR.

[26]  Nick Craswell,et al.  Query Expansion with Locally-Trained Word Embeddings , 2016, ACL.

[27]  W. Bruce Croft,et al.  Embedding-based Query Language Models , 2016, ICTIR.

[28]  Alessandro Moschitti,et al.  Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks , 2015, SIGIR.

[29]  Rui Yan,et al.  Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System , 2016, SIGIR.

[30]  Craig MacDonald,et al.  Using word embeddings in Twitter election classification , 2016, Information Retrieval Journal.

[31]  Bhaskar Mitra,et al.  Exploring Session Context using Distributed Representations of Queries and Reformulations , 2015, SIGIR.

[32]  W. Bruce Croft,et al.  Semantic Matching by Non-Linear Word Transportation for Information Retrieval , 2016, CIKM.

[33]  Mandar Mitra,et al.  Word Embedding based Generalized Language Model for Information Retrieval , 2015, SIGIR.

[34]  Bhaskar Mitra,et al.  Report on the SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR) , 2016, SIGIR Forum.

[35]  Jakob Grue Simonsen,et al.  A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion , 2015, CIKM.

[36]  Utpal Garain,et al.  Using Word Embeddings for Automatic Query Expansion , 2016, ArXiv.

[37]  W. Bruce Croft,et al.  A Deep Relevance Matching Model for Ad-hoc Retrieval , 2016, CIKM.

[38]  Bhaskar Mitra,et al.  Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval , 2016, SIGIR.

[39]  Xueqi Cheng,et al.  Text Matching as Image Recognition , 2016, AAAI.

[40]  Bhaskar Mitra,et al.  Report on the Second SIGIR Workshop on Neural Information Retrieval (Neu-IR'17) , 2018, SIGF.

[41]  Jiafeng Guo,et al.  Analysis of the Paragraph Vector Model for Information Retrieval , 2016, ICTIR.

[42]  Nick Craswell,et al.  Learning to Match using Local and Distributed Representations of Text for Web Search , 2016, WWW.

[43]  Markus Koskela,et al.  Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval , 2016 .

[44]  W. Bruce Croft,et al.  Estimating Embedding Vectors for Queries , 2016, ICTIR.

[45]  Guandong Xu,et al.  Sequence-based context-aware music recommendation , 2018, Information Retrieval Journal.