Mastering NLP with spaCy – Part 3

Rule-based matching in spaCy offers a powerful method for information extraction, serving as a more readable alternative to regular expressions. The `Matcher` class enables the creation of patterns based on token attributes such as text, lemma, part-of-speech tags, and other linguistic features. For efficiently matching against large dictionaries, the `PhraseMatcher` can be used to find specific terms or even patterns based on attributes like word shape. Practical Python code examples demonstrate how to extract entities like greetings, proper names, and IP addresses from text.