Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Natural Language Processing with PyTorch 1.x

You're reading from   Hands-On Natural Language Processing with PyTorch 1.x Build smart, AI-driven linguistic applications using deep learning and NLP techniques

Arrow left icon
Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781789802740
Length 276 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Thomas Dop Thomas Dop
Author Profile Icon Thomas Dop
Thomas Dop
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Section 1: Essentials of PyTorch 1.x for NLP
2. Chapter 1: Fundamentals of Machine Learning and Deep Learning FREE CHAPTER 3. Chapter 2: Getting Started with PyTorch 1.x for NLP 4. Section 2: Fundamentals of Natural Language Processing
5. Chapter 3: NLP and Text Embeddings 6. Chapter 4: Text Preprocessing, Stemming, and Lemmatization 7. Section 3: Real-World NLP Applications Using PyTorch 1.x
8. Chapter 5: Recurrent Neural Networks and Sentiment Analysis 9. Chapter 6: Convolutional Neural Networks for Text Classification 10. Chapter 7: Text Translation Using Sequence-to-Sequence Neural Networks 11. Chapter 8: Building a Chatbot Using Attention-Based Neural Networks 12. Chapter 9: The Road Ahead 13. Other Books You May Enjoy

Future NLP tasks

While the majority of this book has been focused on text classification and sequence generation, there are a number of other NLP tasks that we haven't really touched on. While many of these are more interesting from an academic perspective rather than a practical perspective, it's important to understand these tasks as they form the basis of how language is constructed and formed. Anything we, as NLP data scientists, can do to better understand the formation of natural language will only improve our understanding of the subject matter. In this section, we will discuss, in more detail, four key areas of future development in NLP:

  • Constituency parsing
  • Semantic role labeling
  • Textual entailment
  • Machine comprehension

Constituency parsing

Constituency parsing (also known as syntactic parsing) is the act of identifying parts of a sentence and assigning a syntactic structure to it. This syntactic structure is largely determined by the...

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 $19.99/month. Cancel anytime