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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

Topic models in scikit-learn

Gensim isn't the only package offering us the ability to topic model: scikit-learn, while not dedicated for text, still offers fast implementations of LDA and Non-negative Matrix Factorization (NMF), which can help us identify topics.

We already discussed how LDA works, and the only difference between the Gensim and scikit-learn implementations are as follows:

  1. The perplexity bounds are not expected to agree exactly here because the bound is calculated differently in Gensim versus sklearn. These bounds are ways we calculate how topics converge in topic modeling algorithms.
  2. Sklearn uses cython which creates numerical 6th decimal point differences.

Non-negative matrix factorization (NMF) [15], unlike LDA, is not a method mostly limited to text mining (though interestingly, LDA's variants also have been used in genetics and image processing...

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