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

A look ahead – Python for NLP

Traditionally, NLP has been accomplished with a variety of computer languages, from early, special-purpose languages, such as Lisp and Prolog, to more modern languages, such as Java and now Python. Currently, Python is probably the most popular language for NLP, in part because interesting applications can be implemented relatively quickly and developers can rapidly get feedback on the results of their ideas.

Another major advantage of Python is the very large number of useful, well-tested, and well-documented Python libraries that can be applied to NLP problems. Some of these libraries are NLTK, spaCy, scikit-learn, and Keras, to name only a few. We will be exploring these libraries in detail in the chapters to come. In addition to these libraries, we will also be working with development tools such as JupyterLab. You will also find other resources such as Stack Overflow and GitHub to be extremely valuable.