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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

Summarizing text

Often in text analysis, it is useful to summarize large bodies of text either to have a brief overlook of the text before deeply analyzing it or identifying the keywords in a text. It is also often the end game a text analysis task of its own. We will not be working on building our own text summarization pipeline, but rather focus on using the built-in summarization API which Gensim offers us.

It is important to remember that the algorithms included in Gensim do not create its own sentences, but rather extracts the key sentences from the text which we run the algorithm on. This summarizer is based on the TextRank algorithm, from an article by Mihalcea and others, called TextRank [10]. This algorithm was later improved upon by Barrios and others in another article, Variations of the Similarity Function of TextRank for Automated Summarization ...

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