Python Tutorial: Rule-based Matching

Want to learn more? Take the full course at https://learn.datacamp.com/courses/advanced-nlp-with-spacy at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this video, we'll take a look at spaCy's matcher, which lets you write rules to find words and phrases in the text. Compared to regular expressions, the matcher works with Doc and Token objects instead of only strings. It's also more flexible: you can search for texts but also other lexical attributes. You can even write rules that use the model's predictions. For example, find the word "duck" only if it's a verb, not a noun. Match patterns are lists of dictionaries. Each dictionary describes one token. The keys are the names of token attributes, mapped to their expected values. In this example, we're looking for two tokens with the text "iPhone" and "X". We can also match other token attributes. Here, we're looking for two tokens whose lowercase forms equal "iPhone" and "x". We can even write patterns using attributes predicted by the model. Here, we're matching a token with the lemma "buy", plus a noun. The lemma is the base form, so this pattern would match phrases like "buying milk" or "bought flowers". To use a pattern, we first import the matcher from spacy dot matcher. We also load a model and create the NLP object. The matcher is initialized with the shared vocabulary, NLP dot vocab. You'll learn more about this later – for now, just remember to always pass it in. The matcher dot add method lets you add a pattern. The first argument is a unique ID to identify which pattern was matched. The second argument is an optional callback. We don't need one here, so we set it to None. The third argument is the pattern. To match the pattern on a text, we can call the matcher on any doc. This will return the matches. When you call the matcher on a doc, it returns a list of tuples. Each tuple consists of three values: the match ID, the star