Neural Networks for Information Retrieval
| Authors |
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|---|---|
| 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) |
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| 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 |
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| 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.
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| 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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