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LTG-Oslo Hierarchical Multi-task Network: The importance of negation for document-level sentiment in Spanish

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 نشر من قبل Jeremy Barnes
 تاريخ النشر 2019
  مجال البحث الهندسة المعلوماتية
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This paper details LTG-Oslo teams participation in the sentiment track of the NEGES 2019 evaluation campaign. We participated in the task with a hierarchical multi-task network, which used shared lower-layers in a deep BiLSTM to predict negation, while the higher layers were dedicated to predicting document-level sentiment. The multi-task component shows promise as a way to incorporate information on negation into deep neural sentiment classifiers, despite the fact that the absolute results on the test set were relatively low for a binary classification task.



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