Título |
Aggression Detection in Social Media: Using Deep Neural Networks, Data
Augmentation, and Pseudo Labeling |
Tipo |
Congreso |
Sub-tipo |
Memoria |
Descripción |
Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying |
Resumen |
With the advent of the read-write web which facilitates social interactions in online spaces, the
rise of anti-social behaviour in online spaces has attracted the attention of researchers. In this
paper, we address the challenge of automatically identifying aggression in social media posts.
Our team, saroyehun, participated in the English track of the Aggression Detection in Social
Media Shared Task. On this task, we investigate the efficacy of deep neural network models of
varying complexity. Our results reveal that deep neural network models require more data points
to do better than an NBSVM linear baseline based on character n-grams. Our improved deep
neural network models were trained on augmented data and pseudo labeled examples. Our LSTM
classifier receives a weighted macro-F1 score of 0.6425 to rank first overall on the Facebook subtask of the shared task. On the social media sub-task, our CNN-LSTM model records a weighted
macro-F1 score of 0.5920 to place third overall. |
Observaciones |
http://aclweb.org/anthology/W18-4411,
pages 90–97.
|
Lugar |
Santa Fe |
País |
Estados Unidos |
No. de páginas |
8 |
Vol. / Cap. |
|
Inicio |
2018-08-25 |
Fin |
|
ISBN/ISSN |
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