(2011) seeks to solve NLP tasks without depending on the feature engineering of conventional NLP methods, our architecture is more practical because it integrates the neural networks into a well-tuned conventional method.
our tagger enjoys both the manually explored combinations of discrete features and the automatically learned non-linearity of the continuous features.