activity
20182022
collaborators

7 papers

cs.LG2022

Multi-Label Quantification

Alejandro Moreo, Manuel Francisco, Fabrizio Sebastiani

Quantification, variously called "supervised prevalence estimation" or "learning to quantify", is the supervised learning task of generating predictors of the relative frequencies…

cs.CL2021

Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution

Silvia Corbara, Alejandro Moreo, Fabrizio Sebastiani

It is well known that, within the Latin production of written text, peculiar metric schemes were followed not only in poetic compositions, but also in many prose works. Such metric…

cs.LG2021

QuaPy: A Python-Based Framework for Quantification

Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani

QuaPy is an open-source framework for performing quantification (a.k.a. supervised prevalence estimation), written in Python. Quantification is the task of training quantifiers via…

cs.CL2020

Tweet Sentiment Quantification: An Experimental Re-Evaluation

Alejandro Moreo, Fabrizio Sebastiani

Sentiment quantification is the task of training, by means of supervised learning, estimators of the relative frequency (also called ``prevalence'') of sentiment-related classes (s…

cs.LG2020

Re-Assessing the "Classify and Count" Quantification Method

Alejandro Moreo, Fabrizio Sebastiani

Learning to quantify (a.k.a.\ quantification) is a task concerned with training unbiased estimators of class prevalence via supervised learning. This task originated with the obser…

cs.CL2019

SemEval-2014 Task 9: Sentiment Analysis in Twitter

Sara Rosenthal, Preslav Nakov, Alan Ritter +1

We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year's task that ran successfully as part of SemEval-2013. As in 2…