Search Results for author: Fabrizio Sebastiani

Found 22 papers, 10 papers with code

Measuring Fairness under Unawareness via Quantification

1 code implementation17 Sep 2021 Alessandro Fabris, Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani

In this work, we tackle the problem of measuring group fairness under unawareness of sensitive attributes, by using techniques from quantification, a supervised learning task concerned with directly providing group-level prevalence estimates (rather than individual-level class labels).

Fairness

QuaPy: A Python-Based Framework for Quantification

1 code implementation18 Jun 2021 Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani

prevalence values) of the classes of interest in a sample of unlabelled data.

Model Selection

Tweet Sentiment Quantification: An Experimental Re-Evaluation

1 code implementation4 Nov 2020 Alejandro Moreo, Fabrizio Sebastiani

It is well-known that solving quantification by means of ``classify and count'' (i. e., by classifying all unlabelled items by means of a standard classifier and counting the items that have been assigned to a given class) is less than optimal in terms of accuracy, and that more accurate quantification methods exist.

Sentiment Analysis

Re-Assessing the "Classify and Count" Quantification Method

1 code implementation4 Nov 2020 Alejandro Moreo, Fabrizio Sebastiani

This task originated with the observation that "Classify and Count" (CC), the trivial method of obtaining class prevalence estimates, is often a biased estimator, and thus delivers suboptimal quantification accuracy; following this observation, several methods for learning to quantify have been proposed that have been shown to outperform CC.

General Classification Sentiment Analysis

MedLatinEpi and MedLatinLit: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts

no code implementations22 Jun 2020 Silvia Corbara, Alejandro Moreo, Fabrizio Sebastiani, Mirko Tavoni

We present and make available MedLatinEpi and MedLatinLit, two datasets of medieval Latin texts to be used in research on computational authorship analysis.

Authorship Verification

Word-Class Embeddings for Multiclass Text Classification

1 code implementation26 Nov 2019 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 disambiguation, machine translation, and sentiment analysis, to name a few.

Classification General Classification +6

Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search

no code implementations25 May 2019 Pepa Atanasova, Georgi Karadzhov, Yasen Kiprov, Preslav Nakov, Fabrizio Sebastiani

While typically a user would expect a single response at any utterance, a system could also return multiple options for the user to select from, based on different system understandings of the user's intent.

Question Answering

Cross-Lingual Sentiment Quantification

3 code implementations16 Apr 2019 Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani

Cross-lingual sentiment quantification (and cross-lingual \emph{text} quantification in general) has never been discussed before in the literature; we establish baseline results for the binary case by combining state-of-the-art quantification methods with methods capable of generating cross-lingual vectorial representations of the source and target documents involved.

Cross-Lingual Sentiment Classification General Classification +1

Learning to Weight for Text Classification

1 code implementation28 Mar 2019 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 score reflecting the importance of the term for the document.

Classification General Classification +2

Building Automated Survey Coders via Interactive Machine Learning

no code implementations28 Mar 2019 Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani

We will show that, for the same amount of training effort, interactive learning delivers much better coding accuracy than standard "non-interactive" learning.

Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification

1 code implementation31 Jan 2019 Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani

Funnelling consists of generating a two-tier classification system where all documents, irrespectively of language, are classified by the same (2nd-tier) classifier.

Classification Ensemble Learning +3

Evaluation Measures for Quantification: An Axiomatic Approach

no code implementations6 Sep 2018 Fabrizio Sebastiani

While the scientific community has devoted a lot of attention to devising more accurate quantification methods, it has not devoted much to discussing what properties an \emph{evaluation measure for quantification} (EMQ) should enjoy, and which EMQs should be adopted as a result.

A Recurrent Neural Network for Sentiment Quantification

1 code implementation4 Sep 2018 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 each class c in C. Quantification can in principle be solved by classifying all the unlabelled items and counting how many of them have been attributed to each class.

Optimizing Non-decomposable Measures with Deep Networks

no code implementations31 Jan 2018 Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani

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 the Kullback-Leibler divergence that are structured and non-decomposable.

Online Optimization Methods for the Quantification Problem

no code implementations13 May 2016 Purushottam Kar, Shuai Li, Harikrishna Narasimhan, Sanjay Chawla, Fabrizio Sebastiani

The estimation of class prevalence, i. e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc.

Epidemiology Sentiment Analysis

Utility-Theoretic Ranking for Semi-Automated Text Classification

no code implementations2 Mar 2015 Giacomo Berardi, Andrea Esuli, Fabrizio Sebastiani

\emph{Semi-Automated Text Classification} (SATC) may be defined as the task of ranking a set $\mathcal{D}$ of automatically labelled textual documents in such a way that, if a human annotator validates (i. e., inspects and corrects where appropriate) the documents in a top-ranked portion of $\mathcal{D}$ with the goal of increasing the overall labelling accuracy of $\mathcal{D}$, the expected increase is maximized.

Classification General Classification +1

On the Effects of Low-Quality Training Data on Information Extraction from Clinical Reports

no code implementations19 Feb 2015 Diego Marcheggiani, Fabrizio Sebastiani

While a lot of work has been devoted to devising learning methods that generate more and more accurate information extractors, no work has been devoted to investigating the effect of the quality of training data on the learning process.

Optimizing Text Quantifiers for Multivariate Loss Functions

no code implementations19 Feb 2015 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 a dataset of unlabelled items.

Structured Prediction

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