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
Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

Arrow left icon
Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Understanding TensorFlow 3. Word2vec – Learning Word Embeddings 4. Advanced Word2vec 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Applications of LSTM – Image Caption Generation 10. Sequence-to-Sequence Learning – Neural Machine Translation 11. Current Trends and the Future of Natural Language Processing A. Mathematical Foundations and Advanced TensorFlow Index

New tasks emerging


Now we will investigate several novel areas that have emerged in the recent past. These areas include detecting sarcasm, language grounding (that is, the process of eliciting common sense from natural language), and skimming text.

Detecting sarcasm

Sarcasm is when a person utters something which actually means the opposite of the utterance (for example, I love Mondays!). Detecting sarcasm can even be difficult for humans sometimes, and detecting sarcasm through NLP is an even harder task. Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation [23], Lotem Peled and Roi Reichart, uses NLP for detecting sarcasm in Twitter posts. They first create a dataset of 3,000 tweet pairs, where one tweet is the sarcastic tweet and the other tweet is the decrypted nonsarcastic tweet. The decrypted tweets were created by five human judges who looked at the tweet and came up with the actual meaning. Then they used a monolingual machine translation mechanism...

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 €18.99/month. Cancel anytime