41 citations · 113 across the 29 of their papers we have counts for
34 papers
Geometry-Aware Bayesian Quantification via Compositional Data Analysis
Alejandro Moreo, Pablo González, Juan José del Coz
Accurately estimating the unknown target label distribution is the critical first step for adapting to label shift. This task, widely known as quantification or class prevalence es…
Typoglycemia under the Hood: Investigating Language Models' Understanding of Scrambled Words
Gianluca Sperduti, Alejandro Moreo
Research in linguistics has shown that humans can read words with internally scrambled letters, a phenomenon recently dubbed typoglycemia. Some specific NLP models have recently be…
Quantifying Feature Importance for Online Content Moderation
Benedetta Tessa, Alejandro Moreo, Stefano Cresci +2
Accurately estimating how users respond to moderation interventions is paramount for developing effective and user-centred moderation strategies. However, this requires a clear und…
Transductive Model Selection under Prior Probability Shift
Lorenzo Volpi, Alejandro Moreo, Fabrizio Sebastiani
Transductive learning is a supervised machine learning task in which, unlike in traditional inductive learning, the unlabelled data that require labelling are a finite set and are…
Quantifying Query Fairness Under Unawareness
Thomas Jaenich, Alejandro Moreo, Alessandro Fabris +4
Traditional ranking algorithms are designed to retrieve the most relevant items for a user's query, but they often inherit biases from data that can unfairly disadvantage vulnerabl…
On the Interconnections of Calibration, Quantification, and Classifier Accuracy Prediction under Dataset Shift
Alejandro Moreo
When the distribution of the data used to train a classifier differs from that of the test data, i.e., under dataset shift, well-established routines for calibrating the decision s…
Efficient quantification on large-scale networks
Alessio Micheli, Alejandro Moreo, Marco Podda +3
Network quantification (NQ) is the problem of estimating the proportions of nodes belonging to each class in subsets of unlabelled graph nodes. When prior probability shift is at p…
The \textit{Questio de aqua et terra}: A Computational Authorship Verification Study
Martina Leocata, Alejandro Moreo, Fabrizio Sebastiani
The Questio de aqua et terra is a cosmological treatise traditionally attributed to Dante Alighieri. However, the authenticity of this text is controversial, due to discrepancies w…
Misspellings in Natural Language Processing: A survey
Gianluca Sperduti, Alejandro Moreo
This survey provides an overview of the challenges of misspellings in natural language processing (NLP). While often unintentional, misspellings have become ubiquitous in digital c…
Quantification using Permutation-Invariant Networks based on Histograms
Olaya Pérez-Mon, Alejandro Moreo, Juan José del Coz +1
Quantification, also known as class prevalence estimation, is the supervised learning task in which a model is trained to predict the prevalence of each class in a given bag of exa…
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation
Silvia Corbara, Alejandro Moreo
Authorship Verification (AV) is a text classification task concerned with inferring whether a candidate text has been written by one specific author or by someone else. It has been…
Kernel Density Estimation for Multiclass Quantification
Alejandro Moreo, Pablo González, Juan José del Coz
Several disciplines, like the social sciences, epidemiology, sentiment analysis, or market research, are interested in knowing the distribution of the classes in a population rathe…
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective
Mattia Setzu, Silvia Corbara, Anna Monreale +2
While a substantial amount of work has recently been devoted to enhance the performance of computational Authorship Identification (AId) systems, little to no attention has been pa…
Regularization-Based Methods for Ordinal Quantification
Mirko Bunse, Alejandro Moreo, Fabrizio Sebastiani +1
Quantification, i.e., the task of training predictors of the class prevalence values in sets of unlabeled data items, has received increased attention in recent years. However, mos…
Binary Quantification and Dataset Shift: An Experimental Investigation
Pablo González, Alejandro Moreo, Fabrizio Sebastiani
Quantification is the supervised learning task that consists of training predictors of the class prevalence values of sets of unlabelled data, and is of special interest when the l…
Same or Different? Diff-Vectors for Authorship Analysis
Silvia Corbara, Alejandro Moreo, Fabrizio Sebastiani
We investigate the effects on authorship identification tasks of a fundamental shift in how to conceive the vectorial representations of documents that are given as input to a supe…
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…
LeQua@CLEF2022: Learning to Quantify
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
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 in…
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…
Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification
Alejandro Moreo, Andrea Pedrotti, Fabrizio Sebastiani
\emph{Funnelling} (Fun) is a recently proposed method for cross-lingual text classification (CLTC) based on a two-tier learning ensemble for heterogeneous transfer learning (HTL).…
Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach
Alessandro Fabris, Andrea Esuli, Alejandro Moreo +1
Algorithms and models are increasingly deployed to inform decisions about people, inevitably affecting their lives. As a consequence, those in charge of developing these models mus…
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…
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…
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…