Deep Neural Networks for Named Entity Recognition in Italian
Daniele Bonadiman, Aliaksei Severyn, Alessandro Moschitti · Accademia University Press eBooks · 2015
In this paper, we introduce a Deep Neural Network (DNN) for engineering Named Entity Recognizers (NERs) in Italian. Our network uses a sliding window of word contexts to predict tags. It relies on a simple word-level log-likelihood as a cost function and uses a new recurrent feedback mechanism to ensure that the dependencies between the output tags are properly modeled. The evaluation on the Evalita 2009 benchmark shows that our DNN performs on par with the best NERs, outperforming the state of the art when gazetteer features are used.