Mastering spaCy: An end-to-end practical guide to implementing NLP applications using the Python ecosystem

English | 2021 | ISBN: 978-1800563353 | 356 Pages | PDF, EPUB, MOBI | 39 MB

Build end-to-end industrial-strength NLP models using advanced morphological and syntactic features in spaCy to create real-world applications with ease

Key Features

• Gain an overview of what spaCy offers for natural language processing
• Learn details of spaCy’s features and how to use them effectively
• Work through practical recipes using spaCy

spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy’s features and real-world applications.

You’ll begin by installing spaCy and downloading models, before progressing to spaCy’s features and prototyping real-world NLP apps. Next, you’ll get familiar with visualizing with spaCy’s popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you’ll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You’ll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow’s Keras API together with spaCy. You’ll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results.

By the end of this book, you’ll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.

What you will learn

• Install spaCy, get started easily, and write your first Python script
• Understand core linguistic operations of spaCy
• Discover how to combine rule-based components with spaCy statistical models
• Become well-versed with named entity and keyword extraction
• Build your own ML pipelines using spaCy
• Apply all the knowledge you’ve gained to design a chatbot using spaCy