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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
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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

Answering questions from a long text passage

In the previous recipe, we learned an approach to extract the answer to a question, given a context. This pattern involves the model retrieving the answer from the given context. The model cannot answer a question that is not contained in the context. This does serve a purpose where we want an answer from a given context. This type of question-answering system is defined as Closed Domain Question Answering (CDQA).

There is another system of question answering that can answer questions that are general in nature. These systems are trained on larger corpora. This training provides them with the ability to answer questions that are open in nature. These systems are called Open Domain Question Answering (ODQA) systems.

Getting ready

As part of this recipe, we will use the DeepPavlov (https://deeppavlov.ai) ODQA system to answer an open question. We will use the deeppavlov library along with the Knowledge Base Question Answering (KBQA...

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