Chapter 7: Implementing NLP Applications
In Chapter 6, Recurrent Neural Networks for Demand Prediction, we introduced Recurrent Neural Networks (RNNs) as a family of neural networks that are especially powerful to analyze sequential data. As a case study, we trained a Long Short-Term Memory (LSTM)-based RNN to predict the next value in the time series of consumed electrical energy. However, RNNs are not just suitable for strictly numeric time series, as they have also been applied successfully to other types of time series.
Another field where RNNs are state of the art is Natural Language Processing (NLP). Indeed, RNNs have been applied successfully to text classification, language models, and neural machine translation. In all of these tasks, the time series is a sequence of words or characters, rather than numbers.
In this chapter, we will run a short review of some classic NLP case studies and their RNN-based solutions: a sentiment analysis application, a solution for free...
