Book Image

Python Natural Language Processing Cookbook

By : Zhenya Antić
Book Image

Python Natural Language Processing Cookbook

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)

Visualizing the dependency parse

In this recipe, we will learn how to use the displaCy library and visualize the dependency parse. Details about how to create a dependency parse can be found in Chapter 2, Getting the Dependency Parse. We will create two visualizations, one for a short text and another for a long, multi-sentence text.

Getting ready

The displaCy library is part of the spacy package. You need at least version 2.0.12 of the spacy package. If you don't have spacy, install it using the following command:

pip install spacy

To check the version you have, use the following commands:

>>> import spacy
>>> print(spacy.__version__)
2.3.0
>>> exit()

If your version is lower than 2.0.12, use the following command to upgrade spacy:

pip install -U spacy

To validate that the models you have on your computer are compatible with your new version of spacy, use the following command:

python -m spacy validate

How to do it…...