Alfio Ferrara


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Augustine of Hippo at SemEval-2023 Task 4: An Explainable Knowledge Extraction Method to Identify Human Values in Arguments with SuperASKE
Alfio Ferrara | Sergio Picascia | Elisabetta Rocchetti
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

In this paper we present and discuss the results achieved by the “Augustine of Hippo” team at SemEval-2023 Task 4 about human value detection. In particular, we provide a quantitative and qualitative reviews of the results obtained by SuperASKE, discussing respectively performance metrics and classification errors. Finally, we present our main contribution: an explainable and unsupervised approach mapping arguments to concepts, followed by a supervised classification model mapping concepts to human values.


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What is Done is Done: an Incremental Approach to Semantic Shift Detection
Francesco Periti | Alfio Ferrara | Stefano Montanelli | Martin Ruskov
Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change

Contextual word embedding techniques for semantic shift detection are receiving more and more attention. In this paper, we present What is Done is Done (WiDiD), an incremental approach to semantic shift detection based on incremental clustering techniques and contextual embedding methods to capture the changes over the meanings of a target word along a diachronic corpus. In WiDiD, the word contexts observed in the past are consolidated as a set of clusters that constitute the “memory” of the word meanings observed so far. Such a memory is exploited as a basis for subsequent word observations, so that the meanings observed in the present are stratified over the past ones.


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Unsupervised Detection of Argumentative Units though Topic Modeling Techniques
Alfio Ferrara | Stefano Montanelli | Georgios Petasis
Proceedings of the 4th Workshop on Argument Mining

In this paper we present a new unsupervised approach, “Attraction to Topics” – A2T , for the detection of argumentative units, a sub-task of argument mining. Motivated by the importance of topic identification in manual annotation, we examine whether topic modeling can be used for performing unsupervised detection of argumentative sentences, and to what extend topic modeling can be used to classify sentences as claims and premises. Preliminary evaluation results suggest that topic information can be successfully used for the detection of argumentative sentences, at least for corpora used for evaluation. Our approach has been evaluated on two English corpora, the first of which contains 90 persuasive essays, while the second is a collection of 340 documents from user generated content.