In the field of Natural Language Processing (NLP), spaCy reigns supreme as one of the most popular libraries. It is designed specifically for large-scale information extraction tasks, providing robust implementations for a range of NLP tasks like tokenization, part-of-speech tagging, named entity recognition, and many more.

To get started with spaCy, you need to install the library on your device. You can install spaCy by running the following pip command in your terminal or command prompt:

If the metacharacter ! works in your development environment (such as Jupyter notebooks), you can alternatively use:

Additionally, we need to download a model to perform NLP tasks with spaCy. For this lesson, we will be using en_core_web_sm, a small English language model for spaCy. This command should be executed at the terminal/command prompt.

Once we have spaCy and the English language model installed, we can load the model into our Python environment to start using it. Although spaCy provides larger models with more capabilities, we are using a smaller model for our aims because it is quicker to download and requires less memory.

Check out the following code block that imports the spacy library and loads the English language model.

The nlp object is now a language model capable of performing several NLP tasks.

In this section, we'll dive into how we can use the loaded spaCy model to analyze some text. When we process a piece of text with the model, several operations occur. First, the text is tokenized, or split up into individual words or symbols called tokens. Then, the model performs a range of annotation steps, using statistical models to make predictions about each token - for instance, whether a token is a named entity, or what part of speech a word is.

In this basic example, we're mainly interested in the tokenization process. Let's give it a try:

The output of the above code will be:

This code takes the string "I am learning Natural Language Processing with spaCy", processes it through the NLP pipeline, and then iterates through the resultant doc object, printing out the text of each token. Under the hood, spaCy is tokenizing the string for us.

In this lesson, we went through how to get started with spaCy, from installing the library and language model to loading the model and processing a simple piece of text. The foundational knowledge gained here will serve as a springboard for more advanced topics untouched in this lesson.

In the upcoming lessons, we will delve deeper into building a comprehensive NLP pipeline with spaCy. You will get hands-on experience with tokenization, POS tagging, and lemmatization tasks that are crucial for many NLP applications. I recommend revisiting the concepts and running the code discussed in this lesson to ensure a solid understanding of spaCy's functionalities.

Great job so far, and stay tuned for more NLP adventures with spaCy!