2006
Software
Unknown
VCS Verbatim Coding System (http://www.cnr.it/cnr/news/CnrNews?IDn=1576
Sebastiani F., Esuli A., Fagni T.
Software di monitoraggio della customer satisfaction nella grande azienda
Sebastiani F., Esuli A., Fagni T.
Software di monitoraggio della customer satisfaction nella grande azienda
See at: CNR ExploRA
2006
Other
Unknown
SentiWordNet (http://sentiwordnet.isti.cnr.it/)
Sebastiani F., Esuli A.
Dizionario specialistico della lingua inglese, automaticamente generato, orientato ad applicazioni di opinion mining
Sebastiani F., Esuli A.
Dizionario specialistico della lingua inglese, automaticamente generato, orientato ad applicazioni di opinion mining
See at: CNR ExploRA
2010
Software
Unknown
MP-Boost++
Esuli A.
MPBoost++ is a C++ implementation of MPBoost a variant of the multi-label AdaBoost.MH algorithm that improves its efficacy and efficiency by performing a multiple pivot selection at each boosting iteration.
Esuli A.
MPBoost++ is a C++ implementation of MPBoost a variant of the multi-label AdaBoost.MH algorithm that improves its efficacy and efficiency by performing a multiple pivot selection at each boosting iteration.
See at: CNR ExploRA | www.esuli.it
2013
Contribution to conference
Unknown
Minimizing multivariate loss functions for text quantification
Esuli A., Sebastiani F.
Source: IIR 2013 - 4th Italian Information Retrieval Workshop, Pisa, 16-17 gennaio 2013
Esuli A., Sebastiani F.
Source: IIR 2013 - 4th Italian Information Retrieval Workshop, Pisa, 16-17 gennaio 2013
See at: CNR ExploRA
2014
Software
Unknown
MiPai
Esuli A.
This is the repository for the MiPai project, which provides a reference implementation of the Permutation Prefix Index (PP-Index), along with index and search example programs for various data types.
Esuli A.
This is the repository for the MiPai project, which provides a reference implementation of the Permutation Prefix Index (PP-Index), along with index and search example programs for various data types.
See at: CNR ExploRA
2018
Contribution to conference
Open Access
Polylingual Text Classification via Funnelling
Esuli A., Moreo Fernandez A., Sebastiani F.
[Abstract not available]Source: 9th Italian Information Retrieval Workshop (IIR 2018), Roma, IT, 28-30/05/2018
Esuli A., Moreo Fernandez A., Sebastiani F.
[Abstract not available]Source: 9th Italian Information Retrieval Workshop (IIR 2018), Roma, IT, 28-30/05/2018
See at:
ISTI Repository 
| CNR ExploRA
2009
Journal article
Unknown
Multi-faceted rating of product reviews
Baccianella S., Esuli A., Sebastiani F.
Researchers from ISTI-CNR, Pisa, are working on an automatic rating system for online product reviews based on an analysis of their textual content.Source: ERCIM news 77 (2009): 60–61.
Baccianella S., Esuli A., Sebastiani F.
Researchers from ISTI-CNR, Pisa, are working on an automatic rating system for online product reviews based on an analysis of their textual content.Source: ERCIM news 77 (2009): 60–61.
See at: CNR ExploRA
2010
Journal article
Unknown
Extracting information from free-text mammography reports
Esuli A., Marcheggiani D., Sebastiani F.
Researchers from ISTI-CNR, Pisa, aim at effectively and efficiently extracting information from free-text mammography reports, as a step towards the automatic transformation of unstructured medical documentation into structured data.Source: ERCIM news 82 (2010): 60–61.
Esuli A., Marcheggiani D., Sebastiani F.
Researchers from ISTI-CNR, Pisa, aim at effectively and efficiently extracting information from free-text mammography reports, as a step towards the automatic transformation of unstructured medical documentation into structured data.Source: ERCIM news 82 (2010): 60–61.
See at: CNR ExploRA
2010
Conference article
Unknown
Selecting features for ordinal text classification
Baccianella S., Esuli A., Sebastiani F.
We present four new feature selection methods for ordinal regression and test them against four different baselines on two large datasets of product reviews.Source: 1st Italian Information Retrieval Workshop, pp. 13–14, Padova, IT, 27-28 January 2010
Baccianella S., Esuli A., Sebastiani F.
We present four new feature selection methods for ordinal regression and test them against four different baselines on two large datasets of product reviews.Source: 1st Italian Information Retrieval Workshop, pp. 13–14, Padova, IT, 27-28 January 2010
See at: CNR ExploRA
2006
Report
Open Access
MP-Boost: A Multiple-Pivot Boosting Algorithm and its Application to Text Categorization
Esuli A., Fagni T., Sebastiani F.
AdaBoost.MH is a popular supervised learning algorithm for building multi-label (aka n-of-m) text classifiers. AdaBoost.MH belongs to the family ofSource: ISTI Technical reports, 2006
Esuli A., Fagni T., Sebastiani F.
AdaBoost.MH is a popular supervised learning algorithm for building multi-label (aka n-of-m) text classifiers. AdaBoost.MH belongs to the family ofSource: ISTI Technical reports, 2006
See at:
ISTI Repository | CNR ExploRA
2013
Journal article
Restricted
Utility-theoretic ranking for semi-automated text classification
Berardi G., Esuli A., Sebastiani F.
Researchers from ISTI-CNR, Pisa, have addressed the problem of optimizing the work of human editors who proofcheck the results of an automatic text classifier with the goal of improving the accuracy of the automatically classified document set.Source: ERCIM news 92 (2013): 52–53.
Berardi G., Esuli A., Sebastiani F.
Researchers from ISTI-CNR, Pisa, have addressed the problem of optimizing the work of human editors who proofcheck the results of an automatic text classifier with the goal of improving the accuracy of the automatically classified document set.Source: ERCIM news 92 (2013): 52–53.
See at:
ercim-news.ercim.eu 
| CNR ExploRA
2014
Software
Unknown
TreeBoost
Esuli A.
TreeBoost is a Java implementation of TreeBoost.MH a variant of the multi-label AdaBoost.MH algorithm that exploit the hierarchical relation among categories to improve both the efficacy and efficiency of the classifier.
Esuli A.
TreeBoost is a Java implementation of TreeBoost.MH a variant of the multi-label AdaBoost.MH algorithm that exploit the hierarchical relation among categories to improve both the efficacy and efficiency of the classifier.
See at: CNR ExploRA
2018
Software
Unknown
QuaNet repository
Esuli A., Moreo Fernandez A. D.
This repository contains the Python code implementing the QuaNet (described in https://arxiv.org/pdf/1809.00836.pdf) model for quantification and everything needed to reproduce all experiments.
Esuli A., Moreo Fernandez A. D.
This repository contains the Python code implementing the QuaNet (described in https://arxiv.org/pdf/1809.00836.pdf) model for quantification and everything needed to reproduce all experiments.
See at: github.com | CNR ExploRA
2018
Contribution to conference
Open Access
Market Research, Deep Learning, and Quantification
Esuli A., Moreo Fernandez A., Sebastiani F.
An abstract is not availableSource: Association for Survey Computing Conference on The Application of Artificial Intelligence and Machine Learning to Surveys, London, UK, 15/11/2018
Esuli A., Moreo Fernandez A., Sebastiani F.
An abstract is not availableSource: Association for Survey Computing Conference on The Application of Artificial Intelligence and Machine Learning to Surveys, London, UK, 15/11/2018
See at:
goo.gl | ISTI Repository | CNR ExploRA
2021
Software
Unknown
TwiGet
Esuli A.
TwiGet is a python package for the management of the queries on filtered stream of the Twitter API, and the collection of tweets from it. It can be used as a command line tool (twiget-cli) or as a python class (TwiGet).Project(s): AI4Media
Esuli A.
TwiGet is a python package for the management of the queries on filtered stream of the Twitter API, and the collection of tweets from it. It can be used as a command line tool (twiget-cli) or as a python class (TwiGet).Project(s): AI4Media
See at: github.com | CNR ExploRA
2007
Other
Unknown
ONTOTEXT
Sebastiani F., Esuli A., Fagni T.
ONTOTEXT addresses three key research aspects: (i) annotating documents with semantic and relational information, e.g. properties and facts in which entities are involved (Knowledge Markup); (ii) providing an adequate degree of interoperability of such relational information, with particular attention to the temporal dimension (Knowledge Extraction); (iii) updating and extending the ontologies used for Semantic Web annotation (Ontology Learning).
Sebastiani F., Esuli A., Fagni T.
ONTOTEXT addresses three key research aspects: (i) annotating documents with semantic and relational information, e.g. properties and facts in which entities are involved (Knowledge Markup); (ii) providing an adequate degree of interoperability of such relational information, with particular attention to the temporal dimension (Knowledge Extraction); (iii) updating and extending the ontologies used for Semantic Web annotation (Ontology Learning).
See at: CNR ExploRA
2013
Report
Open Access
The User Feedback on SentiWordNet
Esuli A.
With the release of SentiWordNet 3.0 the related Web interface has been restyled and improved in order to allow users to submit feedback on the SentiWordNet entries, in the form of the suggestion of alternative triplets of values for an entry. This paper reports on the release of the user feedback collected so far and on the plans for the future.Source: ISTI Technical reports, 2013
Esuli A.
With the release of SentiWordNet 3.0 the related Web interface has been restyled and improved in order to allow users to submit feedback on the SentiWordNet entries, in the form of the suggestion of alternative triplets of values for an entry. This paper reports on the release of the user feedback collected so far and on the plans for the future.Source: ISTI Technical reports, 2013
See at:
ISTI Repository | swn.isti.cnr.it | CNR ExploRA
2015
Journal article
Open Access
Optimizing text quantifiers for multivariate loss functions
Esuli A., Sebastiani F.
Quantification - also known as class prior estimation - is the task of estimating the relative frequencies of classes in application scenarios in which such frequencies may change over time. This task is becoming increasingly important for the analysis of large and complex datasets. Researchers from ISTI-CNR, Pisa, are working with supervised learning methods explicitly devised with quantification in mind.Source: ERCIM news 100 (2015): 49–49.
Esuli A., Sebastiani F.
Quantification - also known as class prior estimation - is the task of estimating the relative frequencies of classes in application scenarios in which such frequencies may change over time. This task is becoming increasingly important for the analysis of large and complex datasets. Researchers from ISTI-CNR, Pisa, are working with supervised learning methods explicitly devised with quantification in mind.Source: ERCIM news 100 (2015): 49–49.
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
The interactive classification system
Esuli A.
ISTI-CNR released a new web application for the manual and automatic classification of documents. Human annotators collaboratively label documents with machine learning algorithms that learn from annotators' actions and support the activity with classification suggestions. The platform supports the early stages of document labelling, with the ability to change the classification scheme on the go and to reuse and adapt existing classifiers.Source: ERCIM news (2023): 34–35.
Project(s): AI4Media, SoBigData-PlusPlus
Esuli A.
ISTI-CNR released a new web application for the manual and automatic classification of documents. Human annotators collaboratively label documents with machine learning algorithms that learn from annotators' actions and support the activity with classification suggestions. The platform supports the early stages of document labelling, with the ability to change the classification scheme on the go and to reuse and adapt existing classifiers.Source: ERCIM news (2023): 34–35.
Project(s): AI4Media
, SoBigData-PlusPlus
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2010
Journal article
Open Access
Al and Opinion Mining, Part 2. Sentiment quantification
Esuli A., Sebastiani F.
Opinion mining, a subdiscipline within data mining and computational linguistics, refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online news sources, social media comments, and other user-generated content. This Trends & Controversies department and the previous one include articles on opinion mining from distinguished experts in computer science and information systems. Each article presents a unique innovative research framework, computational methods, and selected results and examples.Source: IEEE intelligent systems 25 (2010): 72–79. doi:10.1109/MIS.2010.94
DOI: 10.1109/mis.2010.94
Metrics:
Esuli A., Sebastiani F.
Opinion mining, a subdiscipline within data mining and computational linguistics, refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online news sources, social media comments, and other user-generated content. This Trends & Controversies department and the previous one include articles on opinion mining from distinguished experts in computer science and information systems. Each article presents a unique innovative research framework, computational methods, and selected results and examples.Source: IEEE intelligent systems 25 (2010): 72–79. doi:10.1109/MIS.2010.94
DOI: 10.1109/mis.2010.94
Metrics:
See at:
ISTI Repository | IEEE Intelligent Systems | ieeexplore.ieee.org | CNR ExploRA