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The Natural Language Processing Workshop

You're reading from   The Natural Language Processing Workshop Confidently design and build your own NLP projects with this easy-to-understand practical guide

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800208421
Length 452 pages
Edition 1st Edition
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Authors (6):
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Sohom Ghosh Sohom Ghosh
Author Profile Icon Sohom Ghosh
Sohom Ghosh
Nipun Sadvilkar Nipun Sadvilkar
Author Profile Icon Nipun Sadvilkar
Nipun Sadvilkar
Rohan Chopra Rohan Chopra
Author Profile Icon Rohan Chopra
Rohan Chopra
Muzaffar Bashir Shah Muzaffar Bashir Shah
Author Profile Icon Muzaffar Bashir Shah
Muzaffar Bashir Shah
Dwight Gunning Dwight Gunning
Author Profile Icon Dwight Gunning
Dwight Gunning
Aniruddha M. Godbole Aniruddha M. Godbole
Author Profile Icon Aniruddha M. Godbole
Aniruddha M. Godbole
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Table of Contents (10) Chapters Close

Preface
1. Introduction to Natural Language Processing 2. Feature Extraction Methods FREE CHAPTER 3. Developing a Text Classifier 4. Collecting Text Data with Web Scraping and APIs 5. Topic Modeling 6. Vector Representation 7. Text Generation and Summarization 8. Sentiment Analysis Appendix

Key Input Parameters for TextRank

We'll be using the gensim library to implement TextRank. The following are the parameters required for this:

  • text: This is the input text.
  • ratio: This is the required ratio of the number of sentences in the summary to the number of sentences in the input text.

The gensim implementation of the TextRank algorithm uses BM25—a probabilistic variation of TF-IDF—for similarity computation in place of the similarity measure described in step 3 of the algorithm. This will be clearer in the following exercise, in which you will summarize text using TextRank.

Exercise 7.02: Performing Summarization Using TextRank

In this exercise, we will use the classic short story, After Twenty Years by O. Henry, which is available on Project Gutenberg, and the first section of the Wikipedia article on Oscar Wilde. We will summarize each text separately so that we have 20% of the sentences in the original text and then have 25% of...

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