A Kernel-based Approach for Irony and Sarcasm Detection in Italian

This paper describes the UNITOR system that participated to the Irony Detection in Italian Tweets task (IronITA) within the context of EvalIta 2018. The system corresponds to a cascade of Support Vector Machine classifiers. Specific features and kernel functions have been proposed to tackle the different subtasks: Irony Classification and Sarcasm Classification. The proposed system ranked first in the Sarcasm Detection subtask (out of 7 submissions), while it ranked sixth (out of 17 submissions) in the Irony Detection task.

2018 · EVALITA Evaluation of NLP and Speech Tools for Italian

SyntNN at SemEval-2018 task 2: is syntax useful for emoji prediction? embedding syntactic trees in multi layer perceptrons

In this paper, we present SyntNN as a way to include traditional syntactic models in multilayer neural networks used in the task of Semeval Task 2 of emoji prediction. The model builds on the distributed tree embedder also known as distributed tree kernel. Initial results are extremely encouraging but additional analysis is needed to overcome the problem of overfitting.

2018 · Proceedings of The 12th International Workshop on Semantic Evaluation