Neural Networks for Information Retrieval

Open Access
Authors
Publication date 2017
Book title SIGIR'17 : proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Book subtitle August 7-11, 2017, Shinjuku, Tokyo, Japan
ISBN (electronic)
  • 9781450350228
Event 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
Pages (from-to) 1403-1406
Publisher New York, NY: Association for Computing Machinery
Organisations
  • Interfacultary Research - Institute for Logic, Language and Computation (ILLC)
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many different approaches for many different IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research. It covers key architectures, as well as the most promising future directions.
Document type Conference contribution
Language English
Published at https://doi.org/10.1145/3077136.3082062
Published at https://arxiv.org/abs/1707.04242
Downloads
1707.04242 (Accepted author manuscript)
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