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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

By : Zhenya Antić, Saurabh Chakravarty
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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

5 (5)
By: Zhenya Antić, Saurabh Chakravarty

Overview of this book

Harness the power of Natural Language Processing (NLP) to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess. You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs. This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust in your NLP models. By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.
Table of Contents (13 chapters)
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Visualizing Text Data

This chapter is dedicated to creating visualizations for the different aspects of NLP work, much of which we have done in previous chapters. Visualizations are important when working with NLP tasks, as they help us to easier see the big picture of the work accomplished.

We will create different types of visualizations, including visualizations of grammar details, parts of speech, and topic models. After working through this chapter, you will be well equipped to create compelling images to show and explain the outputs of various NLP tasks.

These are the recipes you will find in this chapter:

  • Visualizing the dependency parse
  • Visualizing parts of speech
  • Visualizing NER
  • Creating a confusion matrix plot
  • Constructing word clouds
  • Visualizing topics from Gensim
  • Visualizing topics from BERTopic

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