M. de Rijke
- Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval
- SIGIR 2016: 39th international ACM SIGIR conference on Research and development in information retrieval
- Book/source title
- Book/source subtitle
- the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval: Pisa, Italy , July 17-21, 2016
- Number of pages
- New York, NY: Association for Computing Machinery
- ISBN (electronic)
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
In recent years, deep neural networks have yielded significant performance improvements on speech recognition and computer vision tasks, as well as led to exciting breakthroughs in novel application areas such as automatic voice translation, image captioning, and conversational agents. Despite demonstrating good performance on natural language processing (NLP) tasks (e.g., language modelling and machine translation, the performance of deep neural networks on information retrieval (IR) tasks has had relatively less scrutiny. Recent work in this area has mainly focused on word embeddings and neural models for short text similarity.
The lack of many positive results in this area of information retrieval is partially due to the fact that IR tasks such as ranking are fundamentally different from NLP tasks, but also because the IR and neural network communities are only beginning to focus on the application of these techniques to core information retrieval problems. Given that deep learning has made such a big impact, first on speech processing and computer vision and now, increasingly, also on computational linguistics, it seems clear that deep learning will have a major impact on information retrieval and that this is an ideal time for a workshop in this area.
Neu-IR (pronounced "new IR") will be a forum for new research relating to deep learning and other neural network based approaches to IR. The purpose is to provide an opportunity for people to present new work and early results, compare notes on neural network toolkits, share best practices, and discuss the main challenges facing this line of research.
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