Resources

For a description of the 1st edition of this data challenge, which took place in 2022, you are welcome to download one of the following papers:

1. Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani: LeQua@CLEF2022: Learning to Quantify. Proceedings of the 44th European Conference on Information Retrieval (ECIR 2022), Stavanger, NO, pp. 374-381. This is a concise description of the data challenge published before the submissions came in, and which thus focuses only on the setup of the data challenge and does not discuss the results and the submitting systems;
2. Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani, Gianluca Sperduti: A Concise Overview of LeQua@CLEF 2022: Learning to Quantify. Proceedings of the 13th Conference and Labs of the Evaluation Forum (CLEF 2022), Bologna, IT, pp. 362-381. This is as paper (1) above, but published after the submissions came in, which means it also discusses the results and the submitting systems;
3. Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani, Gianluca Sperduti: A Detailed Overview of LeQua@CLEF 2022: Learning to Quantify. Working Notes of the 13th Conference and Labs of the Evaluation Forum (CLEF 2022), Bologna, IT, pp. 1849-1868. This is as paper (2) above, but much more detailed.

If you are interested in research on learning to quantify, you might want to check

• a 2023 book (published open-access by Springer Nature) on learning to quantify, by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, and Fabrizio Sebastiani;
• a 2017 survey of research on learning to quantify, by Pablo González, Alberto Castaño, Nitesh V. Chawla, and Juan José del Coz, published on ACM Computing Surveys;
• the proceedings of the 1st International Workshop on Learning to Quantify (LQ 2021); a report of that workshop has also been published as Juan José del Coz, Pablo González, Alejandro Moreo, and Fabrizio Sebastiani. Report on the 1st International Workshop on Learning to Quantify (LQ 2021). SIGKDD Explorations 24(1):49–51, 2022.
• the proceedings of the 2nd International Workshop on Learning to Quantify (LQ 2022);
• the proceedings of the 3rd International Workshop on Learning to Quantify (LQ 2023); a report of that workshop will soon appear as Mirko Bunse, Pablo González, Alejandro Moreo, and Fabrizio Sebastiani. Report on the 3rd International Workshop on Learning to Quantify (LQ 2023) in the December 2023 issue of the SIGKDD Explorations magazine.
• 4 videorecordings, 90 mins each, of a 2023 course on learning to quantify (including lectures + a hands-on session) delivered by Alejandro Moreo and Fabrizio Sebastiani;
• the QuaPy open-source Python library for learning to quantify. For participating in this data challenge you are welcome to use QuaPy and its tools in any way you might deem suitable. Check paper Alejandro Moreo, Andrea Esuli, and Fabrizio Sebastiani. QuaPy: A Python-based framework for quantification. Proceedings of the 30th ACM International Conference on Knowledge Management (CIKM 2021), Gold Coast, AU, pp. 4534–4543. to learn more about QuaPy.