Humans need to communicate. Out of this basic need combined with the Web, a vast amount of text has been generated on a daily basis. Given the presence of a lot of information allocated in different resources, it becomes vital to enable machines to understand spoken and written texts. This chapter presents how Deep Learning techniques can solve Natural Language Processing (NLP) tasks (e.g., Text Classification and Sentence Summarization), aiming to benefit from the computational power currently available and the low need for feature engineering when using these models. Initially, some essential concepts about NLP and Deep Learning are presented. Then, different pre-processing and textual representation techniques are explained to be used as input in Deep Learning models. Finally, it is shown how to apply the knowledge acquired in real applications of NLP.

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