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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

Topic coherence and evaluating topic models

In the previous sections, we spoke extensively about how topic models, in general, are rather qualitative in nature - it's difficult to put a number on how useful a topic model is. Despite this, there is a need to evaluate topic models, and the most popular method out there is topic coherence - and lucky for us, Gensim has quite an extensive suite of topic coherence methods for us to try out.

What exactly is topic coherence? Briefly put, it is a measure of how interpretable topics are for human beings. There are multiple coherence measures in topic modeling literature, and we won't be going through the theory for these, but the following links should walk you through the theory and intuition, if interested:

  1. What is topic coherence? [9]
  2. Exploring the Space of Topic Coherence Measures [10]

The first link is a Gensim blog post...

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