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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

By : Zhenya Antić
4.4 (18)
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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

4.4 (18)
By: Zhenya Antić

Overview of this book

Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You’ll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you’ll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data. By the end of this NLP book, you’ll have developed the skills to use a powerful set of tools for text processing.
Table of Contents (10 chapters)
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Chapter 8: Visualizing Text Data

In this chapter, we will create different types of visualizations. We will visualize the dependency parse, which will show the grammatical relations between words in a sentence. Then, we will visualize different types of verbs in a text using a bar graph. After that, we will look at visualizing named entities in a text. Next, we will create word clouds from a corpus of text, and finally, we will visualize topics created with Latent Dirichlet Allocation (LDA) model.

These are the recipes you will find in this chapter:

  • Visualizing the dependency parse
  • Visualizing parts of speech
  • Visualizing NER
  • Constructing word clouds
  • Visualizing topics
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