A comprehensive review of deep learning for natural language processing

Deep learning has attracted considerable attention across many natural language processing (NLP) domains. Deep learning models aim to learn embeddings of data with multiple levels of abstraction through multiple layers for either labelled structured input data or unlabelled unstructured input data. Currently, two research trends have emerged in building higher level embeddings. On one hand, a strong trend in deep learning leads towards increasingly powerful and complex models. On the other hand, multi-purpose sentence representation based on simple sums or averages of word vectors was recently shown to be effective. Furthermore, improving the performance of deep learning methods by attention mechanism has become a research hotspot in the last four years. In this paper, we seek to provide a comprehensive review of recent studies in building neural network (NN) embeddings that have been applied to NLP tasks. We provide a walk-through of deep learning evolution and a description of a variety of its architectures. We present and compare the performance of several deep learning models on standard datasets about different NLP tasks. We also present some deep learning challenges for natural language processing.