This report describes an approach to face a task regarding the identification of hate content and stereotypes within tweets. Two models will be shown, both presented to the HaSpeeDe competition proposed by EVALITA 2020. They are based on a Logistic Regression model that takes different types of embedding as input. The best system shows interesting results.In questa relazione viene mostrato un approccio volto ad affrontare un task riguardante l’identificazione di contenuti d’odio e stereotipi all’interno di tweets. Sono stati realizzati due modelli, presentati alla competizione HaSpeeDe proposta da EVALITA 2020. Entrambi si basano su un modello di Logistic Regression che prende in input diversi tipi di embedding. Il miglior sistema evidenzia...
en We present a multi-agent classification solution for identifying misogynous and aggressive conten...
Sources, in the form of selected Facebook pages, can be used as indicators of hate-rich content. Pol...
We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, a...
This paper explains the system developed for the Hate Speech Detection (HaSpeeDe) shared task within...
Hate speech detection has become a crucial mission in many fields. This paper introduces the system ...
In this article, we present the results of applying a Stacking Ensemble method to the problem of hat...
We describe our approach and experiments to tackle Task A of the second edition of HaSpeeDe, within ...
We describe our approach to address Task A of the EVALITA 2020 Hate Speech Detection (HaSpeeDe2) cha...
International audienceThis paper reports on the systems the InriaFBK Team submitted to the EVALITA 2...
This report was written to describe the systems that were submitted by the team “TheNorth” for the H...
This paper describes the system that team YNU_OXZ submitted for EVALITA 2020. We participate in the ...
We describe the systems the RuG Team developed in the context of the Hate Speech Detection Task in I...
We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, a...
The present paper describes two neural network systems used for Hate Speech Detection tasks that mak...
The task of identifying hate speech in social networks has recently attracted considerable interest ...
en We present a multi-agent classification solution for identifying misogynous and aggressive conten...
Sources, in the form of selected Facebook pages, can be used as indicators of hate-rich content. Pol...
We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, a...
This paper explains the system developed for the Hate Speech Detection (HaSpeeDe) shared task within...
Hate speech detection has become a crucial mission in many fields. This paper introduces the system ...
In this article, we present the results of applying a Stacking Ensemble method to the problem of hat...
We describe our approach and experiments to tackle Task A of the second edition of HaSpeeDe, within ...
We describe our approach to address Task A of the EVALITA 2020 Hate Speech Detection (HaSpeeDe2) cha...
International audienceThis paper reports on the systems the InriaFBK Team submitted to the EVALITA 2...
This report was written to describe the systems that were submitted by the team “TheNorth” for the H...
This paper describes the system that team YNU_OXZ submitted for EVALITA 2020. We participate in the ...
We describe the systems the RuG Team developed in the context of the Hate Speech Detection Task in I...
We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, a...
The present paper describes two neural network systems used for Hate Speech Detection tasks that mak...
The task of identifying hate speech in social networks has recently attracted considerable interest ...
en We present a multi-agent classification solution for identifying misogynous and aggressive conten...
Sources, in the form of selected Facebook pages, can be used as indicators of hate-rich content. Pol...
We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, a...