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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? FREE CHAPTER 2. Python Tips for Text Analysis 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

Throughout this chapter, we saw how basic mathematical and information retrieval methods can be used to help identify how similar or dissimilar two text documents are. We also saw how we can extend these methods to any probabilistic distribution as well, such as topic models themselves this can be particularly handy especially when we are working with more topics than we can analyze with the human eye. Summarization is also another useful tool we are now exposed to since it works on the principle of which keywords provide the most information in a passage, we can use this knowledge of keywords to further aid us in building natural language processing pipelines.

We will now move on to more advanced topics involving neural networks and deep learning for textual data. These include methods such as Word2Vec and Doc2Vec, as well as shallow and deep neural...

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