Introduction

spaCy is my go-to library for Natural Language Processing (NLP) tasks. I’d venture to say that’s the case for the majority of NLP experts out there!

Among the plethora of NLP libraries these days, spaCy really does stand out on its own. If you’ve used spaCy for NLP, you’ll know exactly what I’m talking about. And if you’re new to the power of spaCy, you’re about to be enthralled by how multi-functional and flexible this library is.

The factors that work in the favor of spaCy are the set of features it offers, the ease of use, and the fact that the library is always kept up to date.

This tutorial is a crisp and effective introduction to spaCy and the various NLP features it offers. I’d advise you to go through the below resources if you want to learn about the various aspects of NLP:

• Certified Natural Language Processing (NLP) Course
• Ines Montani and Matthew Honnibal – The Brains behind spaCy
• Introduction to Natural Language Processing (Free Course!)

Getting Started with spaCy

If you are new to spaCy, there are a couple of things you should be aware of:

• spaCy’s Statistical Models
• spaCy’s Processing Pipeline

Let’s discuss each one in detail.

spaCy’s Statistical Models

These models are the power engines of spaCy. These models enable spaCy to perform several NLP related tasks, such as part-of-speech tagging, named entity recognition, and dependency parsing.

I’ve listed below the different statistical models in spaCy along with their specifications:

• en_core_web_sm: English multi-task CNN trained on OntoNotes. Size – 11 MB
• en_core_web_md: English multi-task CNN trained on OntoNotes, with GloVe vectors trained on Common Crawl. Size – 91 MB
• en_core_web_lg: English multi-task CNN trained on OntoNotes, with GloVe vectors trained on Common Crawl. Size – 789 MB

Importing these models is super easy. We can import a model by just executing spacy.load(‘model_name’) as shown below:

import spacy
nlp = spacy.load('en_core_web_sm')

spaCy’s Processing Pipeline

The first step for a text string, when working with spaCy, is to pass it to an NLP object. This object is essentially a pipeline of several text pre-processing operations through which the input text string has to go through.

Source: https://course.spacy.io/chapter3

As you can see in the figure above, the NLP pipeline has multiple components, such as tokenizer, tagger, parser, ner, etc. So, the input text string has to go through all these components before we can work on it.

Let me show you how we can create an nlp object:

You can use the below code to figure out the active pipeline components:

nlp.pipe_names

Output: [‘tagger’, ‘parser’, ‘ner’]

Just in case you wish to disable the pipeline components and keep only the tokenizer up and running, then you can use the code below to disable the pipeline components:

nlp.disable_pipes('tagger', 'parser')

Let’s again check the active pipeline component:

nlp.pipe_names

Output: [‘ner’]

When you only have to tokenize the text, you can then disable the entire pipeline. The tokenization process becomes really fast. For example, you can disable multiple components of a pipeline by using the below line of code:

nlp.disable_pipes('tagger', 'parser')

spaCy in Action

1. Part-of-Speech (POS) Tagging using spaCy

Output:

spacy.explain("PART")

2. Dependency Parsing using spaCy

spacy.explain("nsubj"), spacy.explain("ROOT"), spacy.explain("aux"), spacy.explain("advcl"), spacy.explain("dobj")

spacy.explain("NORP")

Let’s see how it works:

So, in the code above:

• First, we import the spaCy matcher
• After that, we initialize the matcher object with the default spaCy vocabulary
• Then, we pass the input in an NLP object as usual
• In the next step, we define the rule/pattern for what we want to extract from the text.

Let’s say we want to extract the phrase “lemon water” from the text. So, our objective is that whenever “lemon” is followed by the word “water”, then the matcher should be able to find this pattern in the text. That’s exactly what we have done while defining the pattern in the code above. Finally, we add the defined rule to the matcher object.

Now let’s see what the matcher has found out:

matches = matcher(doc)
matches

Output: lemon water

So, the pattern is a list of token attributes. For example, ‘TEXT’ is a token attribute that means the exact text of the token. There are, in fact, many other useful token attributes in spaCy which can be used to define a variety of rules and patterns.

I’ve listed below the token attributes:

Source: https://spacy.io/usage/rule-based-matching

Let’s see another use case of the spaCy matcher. Consider the two sentences below:

1. You can read this book
2. I will book my ticket

Now we are interested in finding whether a sentence contains the word “book” in it or not. It seems pretty straight forward right? But here is the catch – we have to find the word “book” only if it has been used in the sentence as a noun.

In the first sentence above, “book” has been used as a noun and in the second sentence, it has been used as a verb. So, the spaCy matcher should be able to extract the pattern from the first sentence only. Let’s try it out:

matches = matcher(doc1)
matches

matches = matcher(doc2)
matches

End Notes