Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning

This chapter will review the most common approaches to natural language understanding (NLU) and discuss both the benefits and drawbacks of each approach, including rule-based techniques, statistical techniques, and deep learning. It will also discuss popular pre-trained models such as Bidirectional Encoder Representations from Transformers (BERT) and its variants. We will learn that NLU is not a single technology; it includes a range of techniques, which are applicable to different goals.

In this chapter, we cover the following main topics:

• Rule-based approaches
• Traditional machine-learning approaches
• Deep learning approaches
• Pre-trained models
• Considerations for selecting technologies

Let’s begin!