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Natural Language Processing and Computational Linguistics

You're reading from   Natural Language Processing and Computational Linguistics A practical guide to text analysis with Python, Gensim, spaCy, and Keras

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838535
Length 306 pages
Edition 1st Edition
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Author (1):
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Bhargav Srinivasa-Desikan Bhargav Srinivasa-Desikan
Author Profile Icon Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan
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Table of Contents (17) Chapters Close

Preface 1. What is Text Analysis? 2. Python Tips for Text Analysis FREE CHAPTER 3. spaCy's Language Models 4. Gensim – Vectorizing Text and Transformations and n-grams 5. POS-Tagging and Its Applications 6. NER-Tagging and Its Applications 7. Dependency Parsing 8. Topic Models 9. Advanced Topic Modeling 10. Clustering and Classifying Text 11. Similarity Queries and Summarization 12. Word2Vec, Doc2Vec, and Gensim 13. Deep Learning for Text 14. Keras and spaCy for Deep Learning 15. Sentiment Analysis and ChatBots 16. Other Books You May Enjoy

Summary

In the previous chapter we introduced our readers to deep learning for text, and in this chapter, we saw how we can leverage the power of deep learning in our own applications, whether we use Keras or spaCy. Knowing how to assign sentiment scores or classify our documents gives us a huge boost when designing intelligent text systems, and with pretrained models, we don't have to perform heavy computations every time we wish to make such a classification. It is now within our capacity to build a strong and varied text analysis pipeline!

In the next chapter, we will discuss two popular text analysis problems—sentiment analysis and building our own chatbot—and what possible approaches we can take to solve these problems.

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