Section 2: spaCy Features

In this section, spaCy will be unveiled and examined in depth by looking at the most powerful and frequently used features. We will uncover linguistic features from syntactic to semantic, provide practical recipes with pattern matching, and advance into the semantics world with word vectors. We will also discuss statistical information extraction methods in detail. We will cover a detailed case study that shows how to combine all spaCy features to create a real-world NLP pipeline.

This section comprises the following chapters:

• Chapter 3, Linguistic Features
• Chapter 4, Rule-Based Matching
• Chapter 5, Working with Word Vectors and Semantic Similarity
• Chapter 6, Putting Everything Together – Semantic Parsing with spaCy