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

Selecting Libraries and Tools for Natural Language Understanding

This chapter will get you set up to process natural language. We will begin by discussing how to install Python, and then we will discuss general software development tools such as JupyterLab and GitHub. We will also review major Python natural language processing (NLP) libraries, including the Natural Language Toolkit (NLTK), spaCy, and TensorFlow/Keras.

Natural language understanding (NLU) technology has benefited from a wide assortment of very capable, freely available tools. While these tools are very powerful, there is no one library that can do all of the NLP tasks needed for all applications, so it is important to understand what the strengths of the different libraries are and how to combine them.

Making the best use of these tools will greatly accelerate any NLU development project. These tools include the Python language itself, development tools such as JupyterLab, and a number of specific natural language...