Antonio Sorgente


2021

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CompNA at SemEval-2021 Task 1: Prediction of lexical complexity analyzing heterogeneous features
Giuseppe Vettigli | Antonio Sorgente
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes the CompNa model that has been submitted to the Lexical Complexity Prediction (LCP) shared task hosted at SemEval 2021 (Task 1). The solution is based on combining features of different nature through an ensambling method based on Decision Trees and trained using Gradient Boosting. We discuss the results of the model and highlight the features with more predictive capabilities.

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EmpNa at WASSA 2021: A Lightweight Model for the Prediction of Empathy, Distress and Emotions from Reactions to News Stories
Giuseppe Vettigli | Antonio Sorgente
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

This paper describes our submission for the WASSA 2021 shared task regarding the prediction of empathy, distress and emotions from news stories. The solution is based on combining the frequency of words, lexicon-based information, demographics of the annotators and personality of the annotators into a linear model. The prediction of empathy and distress is performed using Linear Regression while the prediction of emotions is performed using Logistic Regression. Both tasks are performed using the same features. Our models rank 4th for the prediction of emotions and 2nd for the prediction of empathy and distress. These results are particularly interesting when considered that the computational requirements of the solution are minimal.

2018

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HEI: Hunter Events Interface A platform based on services for the detection and reasoning about events
Antonio Sorgente | Antonio Calabrese | Gianluca Coda | Paolo Vanacore | Francesco Mele
Proceedings of the Workshop Events and Stories in the News 2018

In this paper we present the definition and implementation of the Hunter Events Interface (HEI) System. The HEI System is a system for events annotation and temporal reasoning in Natural Language Texts and media, mainly oriented to texts of historical and cultural contents available on the Web. In this work we assume that events are defined through various components: actions, participants, locations, and occurrence intervals. The HEI system, through independent services, locates (annotates) the various components, and successively associates them to a specific event. The objective of this work is to build a system integrating services for the identification of events, the discovery of their connections, and the evaluation of their consistency. We believe this interface is useful to develop applications that use the notion of story, to integrate data of digital cultural archives, and to build systems of fruition in the same field. The HEI system has been partially developed within the TrasTest project