Book Image

Mastering spaCy

By : Duygu Altınok
Book Image

Mastering spaCy

By: Duygu Altınok

Overview of this book

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.
Table of Contents (15 chapters)
1
Section 1: Getting Started with spaCy
4
Section 2: spaCy Features
9
Section 3: Machine Learning with spaCy

spaCy container objects

At the beginning of this chapter, we saw a list of container objects including Doc, Token, Span, and Lexeme. We already used Token and Doc in our code. In this subsection, we'll see the properties of the container objects in detail.

Using container objects, we can access the linguistic properties that spaCy assigns to the text. A container object is a logical representation of the text units such as a document, a token, or a slice of the document.

Container objects in spaCy follow the natural structure of the text: a document is composed of sentences and sentences are composed of tokens.

We most widely use Doc, Token, and Span objects in development, which represent a document, a single token, and a phrase, respectively. A container can contain other containers, for instance a document contains tokens and spans.

Let's explore each class and its useful properties one by one.

Doc

We created Doc objects in our code to represent the...