Feature embeddings can easily be used in feature-rich sequence labeling algorithms such as conditional random fields or structured perceptron, but our pilot experiments suggest that with sufficiently rich features, classification-based methods can be extremely competitive on these datasets, at a fraction of the computational cost.
we apply a support vector machine (SVM) classifier, Component Feature template Orthography (3) w i contains number, uppercase character, or hyphen adding dense features from FEMA (and the alternative representation learning techniques) to a set of basic features.