This paper describes the UNIBA team participation in the IronITA 2018 task at EVALITA 2018. We propose a supervised approach based on LIBLINEAR that relies on keyword, polarity, microblogging features and representation of tweets in a distributional semantic model. Our system ranked 3rd and 4th in the irony detection subtask. We participated only in the constraint run exploiting the training data provided by the task organizers.
UNIBA - Integrating distributional semantics features in a supervised approach for detecting irony in Italian tweets
Basile, Pierpaolo;Semeraro, Giovanni
2018-01-01
Abstract
This paper describes the UNIBA team participation in the IronITA 2018 task at EVALITA 2018. We propose a supervised approach based on LIBLINEAR that relies on keyword, polarity, microblogging features and representation of tweets in a distributional semantic model. Our system ranked 3rd and 4th in the irony detection subtask. We participated only in the constraint run exploiting the training data provided by the task organizers.File in questo prodotto:
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