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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

TF-IDF

TF-IDF is yet another technique we can learn about to better represent natural language. It is often used in text mining and information retrieval to match documents based on search terms, but can also be used in combination with embeddings to better represent sentences in embedding form. Let's take the following phrase:

This is a small giraffe

Let's say we want a single embedding to represent the meaning of this sentence. One thing we could do is simply average the individual embeddings of each of the five words in this sentence:

Figure 3.28 – Word embeddings

However, this methodology assigns equal weight to all the words in the sentence. Do you think that all the words contribute equally to the meaning of the sentence? This and a are very common words in the English language, but giraffe is very rarely seen. Therefore, we might want to assign more weight to the rarer words. This methodology is known as Term Frequency –...

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