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Natural Language Processing Fundamentals

You're reading from   Natural Language Processing Fundamentals Build intelligent applications that can interpret the human language to deliver impactful results

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789954043
Length 374 pages
Edition 1st Edition
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Authors (2):
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Dwight Gunning Dwight Gunning
Author Profile Icon Dwight Gunning
Dwight Gunning
Sohom Ghosh Sohom Ghosh
Author Profile Icon Sohom Ghosh
Sohom Ghosh
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Table of Contents (10) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Basic Feature Extraction Methods 3. Developing a Text classifier 4. Collecting Text Data from the Web 5. Topic Modeling 6. Text Summarization and Text Generation 7. Vector Representation 8. Sentiment Analysis Appendix

Summary

In this chapter, you have learned about text summarization and text generation. We also looked at various ways in which text summarization can be performed, such as the use of the TextRank algorithm, the Gensim library, and word frequency models. One approach we looked at during this chapter was the use of Markov chains to perform text generation. In the next chapter, we will learn about how text can be represented as vectors. We will also discuss various vector representations, such as Word2Vec and Doc2Vec, in detail.

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