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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

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781789802740
Length 276 pages
Edition 1st Edition
Languages
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Author (1):
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Thomas Dop Thomas Dop
Author Profile Icon Thomas Dop
Thomas Dop
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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

Exploring n-grams

In our CBOW model, we successfully showed that the meaning of the words is related to the context of the words around it. It is not only our context words that influence the meaning of words in a sentence, but the order of those words as well. Consider the following sentences:

The cat sat on the dog

The dog sat on the cat

If you were to transform these two sentences into a bag-of-words representation, we would see that they are identical. However, by reading the sentences, we know they have completely different meanings (in fact, they are the complete opposite!). This clearly demonstrates that the meaning of a sentence is not just the words it contains, but the order in which they occur. One simple way of attempting to capture the order of words within a sentence is by using n-grams.

If we perform a count on our sentences, but instead of counting individual words, we now count the distinct two-word pairings that occur within the sentences, this is known...

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