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

Rule-Based Techniques

Rule-based techniques are a very important and useful tool in natural language processing (NLP). Rules are used to examine text and decide how it should be analyzed in an all-or-none fashion, as opposed to the statistical techniques we will be reviewing in later chapters. In this chapter, we will discuss how to apply rule-based techniques to NLP. We will look at examples such as regular expressions, syntactic parsing, and semantic role assignment. We will primarily use the NLTK and spaCy libraries, which we have seen in earlier chapters.

In this chapter, we will cover the following topics:

  • Rule-based techniques
  • Why use rules?
  • Exploring regular expressions
  • Sentence-level analysis – syntactic parsing and semantic role assignment