8k citations · 8.2k across the 23 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
On the Effects of Low-Quality Training Data on Information Extraction from Clinical Reports
Diego Marcheggiani, Fabrizio Sebastiani
In the last five years there has been a flurry of work on information extraction from clinical documents, i.e., on algorithms capable of extracting, from the informal and unstructu…
Utility-Theoretic Ranking for Semi-Automated Text Classification
Giacomo Berardi, Andrea Esuli, Fabrizio Sebastiani
\emph{Semi-Automated Text Classification} (SATC) may be defined as the task of ranking a set of automatically labelled textual documents in such a way that, if a huma…
Optimizing Text Quantifiers for Multivariate Loss Functions
Andrea Esuli, Fabrizio Sebastiani
We address the problem of \emph{quantification}, a supervised learning task whose goal is, given a class, to estimate the relative frequency (or \emph{prevalence}) of the class in…