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OSIRRC@SIGIR 2019: Paris, France
- Ryan Clancy, Nicola Ferro, Claudia Hauff, Jimmy Lin, Tetsuya Sakai, Ze Zhong Wu:
Proceedings of the Open-Source IR Replicability Challenge co-located with 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, OSIRRC@SIGIR 2019, Paris, France, July 25, 2019. CEUR Workshop Proceedings 2409, CEUR-WS.org 2019 - Ryan Clancy, Nicola Ferro, Claudia Hauff, Jimmy Lin, Tetsuya Sakai, Ze Zhong Wu:
Overview of the 2019 Open-Source IR Replicability Challenge (OSIRRC 2019). 1-7
Position Papers
- Timo Breuer, Philipp Schaer, Narges Tavakolpoursaleh, Johann Schaible, Benjamin Wolff, Bernd Müller:
STELLA: Towards a Framework for the Reproducibility of Online Search Experiments. 8-11 - Sebastian Hofstätter, Allan Hanbury:
Let's measure run time! Extending the IR replicability infrastructure to include performance aspects. 12-16 - Chris Kamphuis, Arjen P. de Vries:
Reproducible IR needs an (IR) (Graph) Query Language. 17-20
Docker Papers
- Negar Arabzadeh:
Entity Retrieval Docker Image for OSIRRC at SIGIR 2019. 21-25 - Arthur Barbosa Câmara, Craig Macdonald:
Dockerising Terrier for The Open-Source IR Replicability Challenge (OSIRRC 2019). 26-30 - Timo Breuer, Philipp Schaer:
Dockerizing Automatic Routing Runs for The Open-Source IR Replicability Challenge (OSIRRC 2019). 31-35 - Ryan Clancy, Zeynep Akkalyoncu Yilmaz, Ze Zhong Wu, Jimmy Lin:
University of Waterloo Docker Images for OSIRRC at SIGIR 2019. 36 - Nicola Ferro, Stefano Marchesin, Alberto Purpura, Gianmaria Silvello:
A Docker-Based Replicability Study of a Neural Information Retrieval Model. 37-43 - Claudia Hauff:
Dockerizing Indri for OSIRRC 2019. 44-46 - Chris Kamphuis, Arjen P. de Vries:
The OldDog Docker Image for OSIRRC at SIGIR 2019. 47-49 - Antonio Mallia, Michal Siedlaczek, Joel M. Mackenzie, Torsten Suel:
PISA: Performant Indexes and Search for Academia. 50-56 - Harrisen Scells, Guido Zuccon:
ielab at the Open-Source IR Replicability Challenge 2019. 57-61 - Zhaohao Zeng, Tetsuya Sakai:
BM25 Pseudo Relevance Feedback Using Anserini at Waseda University. 62-63

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