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LeQua 2022: Learning to Quantify

The aim of LeQua 2022 (the 1st edition of the CLEF “Learning to Quantify” lab) is to allow the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents. These predictors (called “quantifiers”) will be required to issue predictions for several such sets, some of them characterized by class frequencies radically different from the ones of the training set. For a detailed description of this lab you are welcome to download the paper Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani: LeQua@CLEF2022: Learning to Quantify.

If you are interested in research on learning to quantify, consider attending the 1st International Workshop on Learning to Quantify (LQ 2021), which will take place on November 1 and November 5, 2021.

For a survey of research on learning to quantify up to 2017, see Pablo González, Alberto Castaño, Nitesh V. Chawla, Juan José del Coz: A Review on Quantification Learning. ACM Computing Surveys 50(5): 74:1-74:40 (2017); for more recent work, check the forthcoming proceedings of LQ 2021.