Corpus ID: 212737158

Overview of the TREC 2019 deep learning track

@article{Craswell2020OverviewOT,
  title={Overview of the TREC 2019 deep learning track},
  author={Nick Craswell and Bhaskar Mitra and Emine Yilmaz and Daniel Fernando Campos and E. Voorhees},
  journal={ArXiv},
  year={2020},
  volume={abs/2102.07662}
}
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime. It is the first track with large human-labeled training sets, introducing two sets corresponding to two tasks, each with rigorous TREC-style blind evaluation and reusable test sets. The document retrieval task has a corpus of 3.2 million documents with 367 thousand training queries, for which we generate a reusable test set of 43 queries. The passage retrieval task has a corpus… Expand
University of Glasgow Terrier Team at the TREC 2019 Deep Learning Track
Learning Passage Impacts for Inverted Indexes
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 35 REFERENCES
UMass at TREC 2004: Novelty and HARD
Neural Ranking Models with Weak Supervision
Learning to rank for information retrieval
Document Expansion by Query Prediction
An Introduction to Neural Information Retrieval
High accuracy retrieval with multiple nested ranker
On Building Fair and Reusable Test Collections using Bandit Techniques
...
1
2
3
4
...