Yuli Vasiliev - Natural Language Processing with Python and spaCy: A Practical Introduction

Increasingly, when you call the bank or your internet provider, you might hear something like the following on the other end of the line: Hello, I am your digital assistant. Please ask your question. Today, robots can talk to humans using natural language, and theyre getting smarter. Even so, very few people understand how these robots work or how they might use these technologies in their own projects.

Natural language processing (NLP)a branch of artificial intelligence that helps machines understand and respond to human languageis the key technology that lies at the heart of any digital assistant product. This book arms you with the skills you need to start creating your own NLP applications. By the end of this book, youll know how to apply NLP approaches to real-world problems, such as analyzing sentences, capturing the meaning of a text, composing original texts, and even building your own chatbot.

If you want to develop an NLP application, you can choose among a wide range of tools and technologies. All the examples in this book are implemented with Python code that employs the spaCy NLP library for Python. Here are some compelling reasons why you might want to choose Python and spaCy for your NLP development.

Python is a high-level programming language known for the following features:

Simplicity If youre new to programming, Python is a good language with which to start, because its extremely easy to learn. Due to its simplicity, Python allows you to write code that others can easily understand. For example, Pythons simplicity helps chatbot developers collaborate with linguists who dont have much programming experience.

Prevalence Python is one of the most popular languages. The vast majority of the widely used APIs have Python wrappers that you can easily install using the pip installation tool. The ability to install Python wrappers via the pip simplifies the process of obtaining third-party tools you might want to use in your NLP applications.

Significant presence in the AI ecosystem There are a lot of Python libraries available in the AI ecosystem. This availability simplifies the development of your NLP applications, allowing you to choose among a range of libraries to best solve a particular task.

This book uses spaCy, a popular Python library that contains the linguistic data and algorithms youll need to process natural language texts. As youll learn in this book, spaCy is easy to use because it provides container objects that represent elements of natural language texts, such as sentences and words. These objects, in turn, have attributes that represent linguistic features, like parts of speech. At the time of this writing, spaCy offered pretrained models for English, German, Greek, Spanish, French, Italian, Lithuanian, Norwegian Bokml, Dutch, Portuguese, and multiple languages combined. In addition, spaCy offers built-in visualizers that you can invoke programmatically to generate a graphic of the syntactic structure of a sentence or named entities in a document.

The spaCy library also natively supports advanced NLP features that other popular NLP libraries for Python dont. For example, spaCy natively supports word vectors (discussed in detail in ), unlike the Natural Language Toolkit (NLTK). When using the latter, you would need to use a third-party tool like Gensim, a Python implementation of the word2vec algorithm.

With spaCy, you can customize existing models or individual model components, and you can train your own models from scratch to meet your applications requirements (youll learn how to do this in .