8k citations · 8.2k across the 23 of their papers we have counts for
30 papers
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…
Unravelling Interlanguage Facts via Explainable Machine Learning
Barbara Berti, Andrea Esuli, Fabrizio Sebastiani
Native language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has…
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…
MedLatinEpi and MedLatinLit: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts
Silvia Corbara, Alejandro Moreo, Fabrizio Sebastiani +1
We present and make available MedLatinEpi and MedLatinLit, two datasets of medieval Latin texts to be used in research on computational authorship analysis. MedLatinEpi and MedLati…
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…
SemEval-2016 Task 4: Sentiment Analysis in Twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal +2
This paper discusses the fourth year of the ``Sentiment Analysis in Twitter Task''. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure fr…
Word-Class Embeddings for Multiclass Text Classification
Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani
Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambigu…
Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search
Pepa Atanasova, Georgi Karadzhov, Yasen Kiprov +2
In recent years, the proliferation of smart mobile devices has lead to the gradual integration of search functionality within mobile platforms. This has created an incentive to mov…
Cross-Lingual Sentiment Quantification
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
\emph{Sentiment Quantification} (i.e., the task of estimating the relative frequency of sentiment-related classes -- such as \textsf{Positive} and \textsf{Negative} -- in a set of…
Building Automated Survey Coders via Interactive Machine Learning
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
Software systems trained via machine learning to automatically classify open-ended answers (a.k.a. verbatims) are by now a reality. Still, their adoption in the survey coding indus…
Learning to Weight for Text Classification
Alejandro Moreo Fernández, Andrea Esuli, Fabrizio Sebastiani
In information retrieval (IR) and related tasks, term weighting approaches typically consider the frequency of the term in the document and in the collection in order to compute a…
Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
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 do…
Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments
Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani
This paper introduces PyDCI, a new implementation of Distributional Correspondence Indexing (DCI) written in Python. DCI is a transfer learning method for cross-domain and cross-li…
Evaluation Measures for Quantification: An Axiomatic Approach
Fabrizio Sebastiani
Quantification is the task of estimating, given a set of unlabelled items and a set of classes , the prevalence (or `relative…
A Recurrent Neural Network for Sentiment Quantification
Andrea Esuli, Alejandro Moreo Fernández, Fabrizio Sebastiani
Quantification is a supervised learning task that consists in predicting, given a set of classes C and a set D of unlabelled items, the prevalence (or relative frequency) p(c|D) of…
Optimizing Non-decomposable Measures with Deep Networks
Amartya Sanyal, Pawan Kumar, Purushottam Kar +2
We present a class of algorithms capable of directly training deep neural networks with respect to large families of task-specific performance measures such as the F-measure and th…
QT2S: A System for Monitoring Road Traffic via Fine Grounding of Tweets
Noora Al Emadi, Sofiane Abbar, Javier Borge-Holthoefer +2
Social media platforms provide continuous access to user generated content that enables real-time monitoring of user behavior and of events. The geographical dimension of such user…