# LeQua@CLEF2022: Learning to Quantify

arXiv: https://arxiv.org/abs/2111.11249 · HTML: https://mlsanity.com/paper/2111.11249/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Class-prior Estimation for Learning from Positive and Unlabeled Data](https://mlsanity.com/paper/1611.01586/) (2016)
- [QuaPy: A Python-Based Framework for Quantification](https://mlsanity.com/paper/2106.11057/) (2021)
- [Evaluation Measures for Quantification: An Axiomatic Approach](https://mlsanity.com/paper/1809.01991/) (2018)
- [A Recurrent Neural Network for Sentiment Quantification](https://mlsanity.com/paper/1809.00836/) (2018)
- [Optimizing Text Quantifiers for Multivariate Loss Functions](https://mlsanity.com/paper/1502.05491/) (2015)

## Built on by (most cited first, 2 of 2)

- [Regularization-Based Methods for Ordinal Quantification](https://mlsanity.com/paper/2310.09210/) (2023)
- [Multi-Label Quantification](https://mlsanity.com/paper/2211.08063/) (2022)

## Abstract (arXiv)

LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents. While these predictions could be easily achieved by first classifying all documents via a text classifier and then counting the numbers of documents assigned to the classes, a growing body of literature has shown this approach to be suboptimal, and has proposed better methods. The goal of this lab is to provide a setting for the comparative evaluation of methods for learning to quantify, both in the binary setting and in the single-label multiclass setting. For each such setting we provide data either in ready-made vector form or in raw document form.