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[email protected]: Learning to Quantify


Nov 30, 2021
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani


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Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution


Oct 27, 2021
Silvia Corbara, Alejandro Moreo, Fabrizio Sebastiani


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Measuring Fairness under Unawareness via Quantification


Sep 17, 2021
Alessandro Fabris, Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani


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QuaPy: A Python-Based Framework for Quantification


Jun 18, 2021
Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani


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Re-Assessing the "Classify and Count" Quantification Method


Nov 04, 2020
Alejandro Moreo, Fabrizio Sebastiani


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Tweet Sentiment Quantification: An Experimental Re-Evaluation


Nov 04, 2020
Alejandro Moreo, Fabrizio Sebastiani


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MedLatin1 and MedLatin2: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts


Jun 22, 2020
Silvia Corbara, Alejandro Moreo, Fabrizio Sebastiani, Mirko Tavoni


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SemEval-2016 Task 4: Sentiment Analysis in Twitter


Dec 03, 2019
Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, Veselin Stoyanov

* SemEval-2016 
* Sentiment analysis, sentiment towards a topic, quantification, microblog sentiment analysis; Twitter opinion mining. arXiv admin note: text overlap with arXiv:1912.00741 

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Word-Class Embeddings for Multiclass Text Classification


Nov 26, 2019
Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani


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Cross-Lingual Sentiment Quantification


Apr 16, 2019
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani


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


Apr 16, 2019
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani

* Forthcoming in the ACM Transactions on Information Systems, 2019 
* 28 pages, 4 figures 

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Building Automated Survey Coders via Interactive Machine Learning


Mar 28, 2019
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani

* To appear in the International Journal of Market Research 

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Learning to Weight for Text Classification


Mar 28, 2019
Alejandro Moreo Fernández, Andrea Esuli, Fabrizio Sebastiani

* To appear in IEEE Transactions on Knowledge and Data Engineering 

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


Jan 31, 2019
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani

* 28 pages, 4 figures 

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Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments


Oct 19, 2018
Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani


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Evaluation Measures for Quantification: An Axiomatic Approach


Sep 06, 2018
Fabrizio Sebastiani

* 36 pages, 2 figures. Submitted for publication in the Information Retrieval Journal 

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A Recurrent Neural Network for Sentiment Quantification


Sep 04, 2018
Andrea Esuli, Alejandro Moreo Fernández, Fabrizio Sebastiani

* Accepted for publication at CIKM 2018 

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Optimizing Non-decomposable Measures with Deep Networks


Jan 31, 2018
Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani


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Online Optimization Methods for the Quantification Problem


Jun 13, 2016
Purushottam Kar, Shuai Li, Harikrishna Narasimhan, Sanjay Chawla, Fabrizio Sebastiani

* 26 pages, 6 figures. A short version of this manuscript will appear in the proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2016 

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Optimizing Text Quantifiers for Multivariate Loss Functions


Apr 15, 2015
Andrea Esuli, Fabrizio Sebastiani

* In press in ACM Transactions on Knowledge Discovery from Data, 2015 

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


Mar 04, 2015
Diego Marcheggiani, Fabrizio Sebastiani

* Submitted for publication 

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Utility-Theoretic Ranking for Semi-Automated Text Classification


Mar 02, 2015
Giacomo Berardi, Andrea Esuli, Fabrizio Sebastiani

* Forthcoming on ACM Transactions on Knowledge Discovery from Data 

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Machine Learning in Automated Text Categorization


Oct 26, 2001
Fabrizio Sebastiani

* Accepted for publication on ACM Computing Surveys 

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