However,<cite> Collobert et al. (2011b)</cite> proposed an effective neural network model that requires little feature engineering and instead learns important features from word embeddings trained on large quantities of unlabelled text -an approach made possible by recent advancements in unsupervised learning of word embeddings on massive amounts of data (Collobert and Weston, 2008; Mikolov et al., 2013) and neural network training algorithms permitting deep architectures (Rumelhart et al., 1986) .