Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naïvely” classifying each document via it... read more of Generalized Funnelling

The Interactive Classification System (ICS), is a web-based application that supports the activity of manual text classification, i.e., labeling documents according to their content. read more of ICS: Interactive Classification System

This data set comprises a labelled training set used in the experimentation of the paper "Binary Quantification and Dataset Shift: An Experimental Investigation". read more of Product Reviews Dataset

Word-Class Embeddings (WCEs) are a form of supervised embeddings specially suited for multiclass text classification. WCEs are meant to be used as extensions (i.e., by concatenation) to pre-trained embeddings (e.g., GloVe or word2vec) embeddings in order ... read more of Word-Class Embeddings for Multiclass Text Classification

QuaPy is an open source framework for quantification (a.k.a. supervised prevalence estimation, or learning to quantify) written in Python. read more of QuaPy

ql4facct is a software for replicating experiments concerning the evaluation of estimators of classifier "fairness". read more of A quantification-based method for the estimation of algorithmic fairness

This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. It appears in our research paper "Ordinal Quantification Through Regularization", which we have published at ECML-PKDD 2022. read more of Product Reviews for Ordinal Quantification

The aim of the LeQua 2022 dataset 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 ... read more of Learning to quantify: LeQua 2022 datasets

This record consists of several scripts and files designed to calculate and visualize the efficiency of a burner, as well as predict the burner power using a neural network model. This model consists of a sequential neural network with multiple layers. Bo... read more of self-x-AIPI - Asphalt Burner power predictor and Efficiency calculator: V1.0.0

Semantical production description using OWL ontologies as enabler for semantic matchmaking. read more of DIH4AI: I-PRAG-7 Semantics for Production Feasibility