Natural Language Processing with spaCy: Extracting insights and entities from unstructured text data (Paperback) (ISBN-13: 9798248082988)

Raw text is messy. It's full of slang, typos, and ambiguity. But buried in that chaos is valuable data.

Natural Language Processing with spaCy is the engineer's guide to turning unstructured text into structured insights. While academic books focus on the math of language, this book focuses on the application. It teaches you to use spaCy, the industrial-strength NLP library, to build pipelines that are fast, accurate, and production-ready.

This is the definitive guide to extracting "Who, What, Where, and When" from millions of documents in seconds. You will learn to parse complex sentences, identify custom entities, and classify text with state-of-the-art accuracy.

Whether you are analyzing customer reviews, automating support tickets, or building a legal tech startup, this book provides the blueprint.

• Named Entity Recognition (NER): Train custom models to detect specific entities relevant to your domain-like "drug names" in medical records or "stock tickers" in financial news.

• Text Classification: Build classifiers that can instantly tag emails as "Urgent," "Spam," or "Sales Lead" with high precision.

• Rule-Based Matching: Combine statistical models with linguistic rules to catch patterns that pure machine learning often misses.

• Dependency Parsing: Understand the grammatical structure of a sentence to extract relationships (e.g., Subject -> Verb -> Object) and build knowledge graphs.

• Production Pipelines: Learn to package your NLP models as deployable Python packages and optimize them for speed on CPU and GPU.

Whether you are a data scientist tired of slow, academic libraries, or a developer needing to add "smarts" to your app, this book is your toolkit for modern NLP.

Don't just read text. Understand it. Scroll up and grab your copy to master the industry standard for Natural Language Processing.