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

Why visualize?

Visualizing data means displaying data in a graphical format such as a chart or graph. This is almost always a useful precursor to training a natural language processing (NLP) system to perform a specific task because it is typically very difficult to see patterns in large amounts of text data. It is often much easier to see overall patterns in data visually. These patterns might be very helpful in making decisions about the most applicable text-processing techniques.

Visualization can also be useful in understanding the results of NLP analysis and deciding what the next steps might be. Because looking at the results of NLP analysis is not an initial exploratory step, we will postpone this topic until Chapter 13 and Chapter 14.

In order to explore visualization, in this chapter, we will be working with a dataset of text documents. The text documents will illustrate a binary classification problem, which will be described in the next section.

Text document dataset...