Fabrizio Sebastiani (0000-0003-4221-6427)

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Biography

Fabrizio Sebastiani is a Director of Research at the Institute for the Science and Technologies of Information of the Italian National Council of Research (ISTI-CNR), where he leads the AI4Text group; formerly he was a Principal Scientist at the Qatar Computing Research Institute (2014/16), and an Associate Professor at the Department of Pure and Applied Mathematics of the University of Padova, Italy (2005/06).

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Consiglio Nazionale delle Ricerche: Pisa, IT

2019-10-01 to present | Director of Research (Istituto di Scienza e Tecnologie dell'Informazione)

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Fabrizio Sebastiani

https://orcid.org/0000-0003-4221-6427

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Biography

Activities

Employment (4)

Consiglio Nazionale delle Ricerche: Pisa, IT

Consiglio Nazionale delle Ricerche: Pisa, IT

Qatar Foundation: Doha, QA

Università di Padova: Padova, IT

Works (50 of 674)

A Simple Method for Classifier Accuracy Prediction Under Prior Probability Shift

A Noise-Oriented and Redundancy-Aware Instance Selection Framework

Explainable Authorship Identification in Cultural Heritage Applications

Multi-Label Quantification

Same or Different? Diff-Vectors for Authorship Analysis

Advanced Topics

Applications of Quantification

Evaluation of Quantification Algorithms

Learning to Quantify

Methods for Learning to Quantify

Ordinal Quantification Through Regularization

The Case for Quantification

The Quantification Landscape

The Road Ahead

Unravelling interlanguage facts via explainable machine learning

Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification

Syllabic quantity patterns as rhythmic features for Latin authorship attribution

LeQua@CLEF2022: Learning to Quantify

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

Tweet sentiment quantification: An experimental re-evaluation

Re-assessing the “Classify and Count” Quantification Method

Word-class embeddings for multiclass text classification

Funnelling

Building automated survey coders via interactive machine learning

Learning to Weight for Text Classification

Jointly Minimizing the Expected Costs of Review for Responsiveness and Privilege in E-Discovery

QT2S: A System for Monitoring Road Traffic via Fine Grounding of Tweets

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

Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification

Distributional random oversampling for imbalanced text classification

Evaluation Measures for the SemEval-2016 Task 4 ``Sentiment Analysis in Twitter''

From classification to quantification in tweet sentiment analysis

General chairs' welcome

Lightweight random indexing for polylingual text classification

Online optimization methods for the quantification problem

Ordinal text quantification

Proceedings of SIGIR 2016, 39th International ACM SIGIR conference on Research and Development in Information Retrieval, Pisa, IT

Proceedings of the 39th ACM Conference on Research and Development in Information Retrieval (SIGIR 2016)

QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification

QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification

Real-Time Location Extraction for Social-Media Events in Qatar

SemEval-2016 Task 4: Sentiment Analysis in Twitter

SemEval-2016 Task 4: Sentiment Analysis in Twitter

Text quantification

The Challenge of Sentiment Quantification

The Challenge of Sentiment Quantification

Transductive Distributional Correspondence Indexing for cross-domain topic classification

Transductive Distributional Correspondence Indexing for Cross-Domain Topic Classification

Transductive Distributional Correspondence Indexing for Cross-Domain Topic Classification

An axiomatically derived measure for the evaluation of classification algorithms

Peer review (2 reviews for 2 publications/grants)