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Python Natural Language Processing Cookbook

You're reading from   Python Natural Language Processing Cookbook Over 60 recipes for building powerful NLP solutions using Python and LLM libraries

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781803245744
Length 312 pages
Edition 2nd Edition
Languages
Concepts
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Authors (2):
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Saurabh Chakravarty Saurabh Chakravarty
Author Profile Icon Saurabh Chakravarty
Saurabh Chakravarty
Zhenya Antić Zhenya Antić
Author Profile Icon Zhenya Antić
Zhenya Antić
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Learning NLP Basics 2. Chapter 2: Playing with Grammar FREE CHAPTER 3. Chapter 3: Representing Text – Capturing Semantics 4. Chapter 4: Classifying Texts 5. Chapter 5: Getting Started with Information Extraction 6. Chapter 6: Topic Modeling 7. Chapter 7: Visualizing Text Data 8. Chapter 8: Transformers and Their Applications 9. Chapter 9: Natural Language Understanding 10. Chapter 10: Generative AI and Large Language Models 11. Index 12. Other Books You May Enjoy

Detecting sentence entailment

In this recipe, we will explore techniques to detect textual entailment, given a set of two sentences. The first sentence in the set is the premise, which sets up a context. The second sentence is the hypothesis, which serves as the claim. Textual entailment identifies the contextual relationship between the premise and the hypothesis. These relationships can be of three types, defined as follows:

  • Entailment – The hypothesis supports the premise
  • Contradiction – The hypothesis contradicts the premise
  • Neutral – The hypothesis neither supports nor contradicts the premise

Getting ready

We will use the Transformers library to detect text entailment. You can use the 9.6_textual_entailment.ipynb notebook from the code site if you need to work from an existing notebook.

How to do it...

In this recipe, we will initialize different sets of sentences that are related through each of the previously defined relationships...

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