This chapter will demonstrate how to use deep learning for natural language processing (NLP). NLP is the processing of human language text. NLP is a broad term for a number of different tasks involving text data, which include (but are not limited to) the following:
- Document classification: Classifying documents into different categories based on their subject
- Named entity recognition: Extracting key information from documents, for example, people, organizations, and locations
- Sentiment analysis: Classifying comments, tweets, or reviews as positive or negative sentiment
- Language translation: Translating text data from one language to another
- Part of speech tagging: Assigning the type to each word in a document, which is usually used in conjunction with another task
In this chapter, we will look at document classification, which is probably the most common NLP technique. This chapter follows a different structure to previous chapters, as we will be looking at a single use case (text classification) but applying multiple approaches to it. This chapter will cover:
- How to perform text classification using traditional machine learning techniques
- Word vectors
- Comparing traditional text classification and deep learning
- Advanced deep learning text classification including 1D convolutionals, RNNs, LSTMs and GRUs