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Learning to quantify: LeQua 2022 datasets

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.

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Personal Data Attributes

Description: Personal Data related Information

Field Value
Anonymised Pseudo Anonymized
ChildrenData No
Cross Border Authorised Yes
Data Protection Impact Assessment Yes
Ethics Committee Approval Yes
General Data Yes
Informed Consent Template Yes
Personal Data No
Personal data was manifestly made public by the data subject No
Sensitive Data No
Additional Info
Field Value
Accessibility Both
Accessibility Mode Download
Availability On-Line
Basic rights Download
Creation Date 2021-12-01
Creator Moreo, Alejandro, [email protected], orcid.org/0000-0002-0377-1025
Dataset Citation Esuli, Andrea, Moreo, Alejandro, & Sebastiani, Fabrizio. (2021). Learning to quantify: LeQua 2022 datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6546188
Dataset Re-Use Safeguards None
DiskSize 104.3
External Identifier 10.5281/zenodo.6546188
Field/Scope of use Non-commercial research only
Format zip
Group Others
Language eng, English
License term 2021-12-01 /2029-12-31
Manifestation Type Virtual
Processing Degree Secondary
Retention Period 2029-12-31 /2030-12-31
Size 104.3GB
Sublicense rights No
Territory of use World Wide
Thematic Cluster Text and Social Media Mining [TSMM]
system:type Dataset
Management Info
Field Value
Author Moreo Alejandro
Maintainer Moreo Alejandro
Version 1
Last Updated 24 June 2023, 01:08 (CEST)
Created 3 March 2023, 18:02 (CET)