Book Image

Natural Language Understanding with Python

By : Deborah A. Dahl
5 (1)
Book Image

Natural Language Understanding with Python

5 (1)
By: Deborah A. Dahl

Overview of this book

Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications. with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future. By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.
Table of Contents (21 chapters)
1
Part 1: Getting Started with Natural Language Understanding Technology
4
Part 2:Developing and Testing Natural Language Understanding Systems
16
Part 3: Systems in Action – Applying Natural Language Understanding at Scale

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!