This chapter will introduce you to the basics of text processing with spaCy. You'll learn about the data structures, how to work with statistical models, and how to use them to predict linguistic features in your text.

Introduction to spaCy

Getting Started

Documents, spans and tokens

Lexical attributes

Statistical models

Model packages

Loading models

Predicting linguistic annotations

Predicting named entities in context

Rule-based matching

Using the Matcher

Writing match patterns

Finding words, phrases, names and concepts

In this chapter, you'll use your new skills to extract specific information from large volumes of text. You'll learn how to make the most of spaCy's data structures, and how to effectively combine statistical and rule-based approaches for text analysis.

Data Structures (1)

Strings to hashes

Vocab, hashes and lexemes

Data Structures (2)

Creating a Doc

Docs, spans and entities from scratch

Data structures best practices

Word vectors and similarity

Inspecting word vectors

Comparing similarities

Combining models and rules

Debugging patterns (1)

Debugging patterns (2)

Efficient phrase matching

Extracting countries and relationships

Large-scale data analysis with spaCy

This chapter will show you to everything you need to know about spaCy's processing pipeline. You'll learn what goes on under the hood when you process a text, how to write your own components and add them to the pipeline, and how to use custom attributes to add your own meta data to the documents, spans and tokens.

Processing pipelines

What happens when you call nlp?

Inspecting the pipeline

Custom pipeline components

Use cases for custom components

Simple components

Complex components

Extension attributes

Setting extension attributes (1)

Setting extension attributes (2)

Entities and extensions

Components with extensions

Scaling and performance

Processing streams

Processing data with context

Selective processing

Processing Pipelines

In this chapter, you'll learn how to update spaCy's statistical models to customize them for your use case – for example, to predict a new entity type in online comments. You'll write your own training loop from scratch, and understand the basics of how training works, along with tips and tricks that can make your custom NLP projects more successful.

Training and updating models

Purpose of training

Creating training data (1)

Creating training data (2)

The training loop

Setting up the pipeline

Building a training loop

Exploring the model

Training best practices

Good data vs. bad data

Training multiple labels

Wrapping up

Training a neural network model

If you're working with a lot of text, you'll eventually want to know more about it. For example, what's it about? What do the words mean in context? Who is doing what to whom? What companies and products are mentioned? Which texts are similar to each other? In this course, you'll learn how to use spaCy, a fast-growing industry standard library for NLP in Python, to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Introduction to Natural Language Processing in Python

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Advanced NLP with spaCy

Good data vs. bad data

Advanced NLP with spaCy

Exercice interactif pratique