Natural Language Processing With spaCy in Python – Real Python

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Rule-based matching is one of the steps in extracting information from unstructured text. It's used to identify and extract tokens and phrases according to patterns (such as lowercase) and grammatical features (such as part of speech). Rule-based matching can use regular expressions to extract entities (such as phone numbers) from an unstructured text. It's different from extracting text using regular expressions only in the sense that regular expressions don't consider the lexical and grammatical attributes of the text. In this example, pattern is a list of objects that defines the combination of tokens to be matched. Both POS tags in it are PROPN (proper noun).

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