Search icon
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Natural Language Understanding with Python

You're reading from  Natural Language Understanding with Python

Product type Book
Published in Jun 2023
Publisher Packt
ISBN-13 9781804613429
Pages 326 pages
Edition 1st Edition
Languages
Author (1):
Deborah A. Dahl Deborah A. Dahl
Profile icon Deborah A. Dahl

Table of Contents (21) Chapters

Preface Part 1: Getting Started with Natural Language Understanding Technology
Chapter 1: Natural Language Understanding, Related Technologies, and Natural Language Applications Chapter 2: Identifying Practical Natural Language Understanding Problems Part 2:Developing and Testing Natural Language Understanding Systems
Chapter 3: Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning Chapter 4: Selecting Libraries and Tools for Natural Language Understanding Chapter 5: Natural Language Data – Finding and Preparing Data Chapter 6: Exploring and Visualizing Data Chapter 7: Selecting Approaches and Representing Data Chapter 8: Rule-Based Techniques Chapter 9: Machine Learning Part 1 – Statistical Machine Learning Chapter 10: Machine Learning Part 2 – Neural Networks and Deep Learning Techniques Chapter 11: Machine Learning Part 3 – Transformers and Large Language Models Chapter 12: Applying Unsupervised Learning Approaches Chapter 13: How Well Does It Work? – Evaluation Part 3: Systems in Action – Applying Natural Language Understanding at Scale
Chapter 14: What to Do If the System Isn’t Working Chapter 15: Summary and Looking to the Future Index Other Books You May Enjoy

Sentence-level analysis

Sentences can be analyzed in terms of their syntax (the structural relationships among parts of the sentence) or their semantics (the relationships among the meanings of the parts of the sentence). We’ll look at both of these types of analysis next. Recognizing syntactic relationships is useful on its own for applications such as grammar checking (does the subject of the sentence agree with the verb? Is the correct form of the verb being used?), while recognizing semantic relationships on their own is useful for applications such as finding the components of a request in chatbots. Recognizing both syntactic and semantic relationships together is an alternative to statistical methods in almost any NLP application.

Syntactic analysis

The syntax of sentences and phrases can be analyzed in a process called parsing. Parsing is a type of analysis that attempts to match a set of rules, called grammar, to an input text. There are many approaches to parsing...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}