(2011), which reached 88.90% accuracy with a mix of unsupervised and supervised techniques to learn word vectors capturing semantic termdocument information, as well as rich sentiment content.
deep-learning architectures have shown their potential in several supervised NLP tasks, by using these word embeddings.