Abstract
In this paper, we describe two systems for predicting message-level offensive language in German tweets: one discriminates between offensive and not offensive messages, and the second performs a fine-grained classification by recognizing also classes of offense. Both systems are based on the same approach, which builds upon Recurrent Neural Networks used with the following features: word embeddings, emoji embeddings and social-network specific features. The model is able to combine word-level information and tweet-level information in order to perform the classification tasks.
| Lingua originale | Inglese |
|---|---|
| Titolo della pubblicazione ospite | Proceedings of the GermEval 2018 Workshop |
| Pagine | 80-84 |
| Numero di pagine | 5 |
| Stato di pubblicazione | Pubblicato - 2018 |
| Evento | GermEval 2018 - Vienna, Austria Durata: 21 dic 2018 → 21 dic 2018 |
Workshop
| Workshop | GermEval 2018 |
|---|---|
| Città | Vienna, Austria |
| Periodo | 21/12/18 → 21/12/18 |
Keywords
- hate speech detection, neural networks, evaluation campaign
Fingerprint
Entra nei temi di ricerca di 'InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks'. Insieme formano una fingerprint unica.Cita questo
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver