Rule-based matching

Before deep learning and statistical modeling took over, NLP was all about rules. That's not to say that rule-based systems are dead! They are often easy to set up and perform very well when it comes to doing simple tasks.

Imagine you wanted to find all mentions of Google in a text. Would you really train a neural network-based named entity recognizer? If you did, you would have to run all of the text through the neural network and then look for Google in the entity texts. Alternatively, would you rather just search for text that exactly matches Google with a classic search algorithm? Well, we're in luck, as spaCy comes with an easy-to-use, rule-based matcher that allows us to do just that.

Before we start this section, we first must make sure that we reload the English language model and import the matcher. This is a very simple task that can be done by running the following code:

import spacy
from spacy.matcher import Matcher

nlp = spacy.load('en')

The matcher searches...