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Mastering spaCy

Solution: Rule-Based Matchmaking

Let's look at the solution to the previous exercise.

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Solution

The solution to the previous exercise is given below:

Python 3.5
import spacy
from spacy.matcher import Matcher
nlp = spacy.load("en_core_web_md")
pattern = [{"IS_DIGIT": True, "LENGTH": 4}, {"TEXT": "-"}, {"IS_DIGIT": True, "LENGTH": 2}, {"TEXT": "-"}, {"IS_DIGIT": True, "LENGTH": 2}]
# Create a Spacy Matcher object and add the pattern to it
matcher = Matcher(nlp.vocab)
matcher.add("DATE_PATTERN", [pattern])
doc = nlp("I have a meeting on 2022-02-15 and another meeting on 2022-02-20")
# Match the defined pattern against the processed document and print out the matched spans
matches = matcher(doc)
for match_id, start, end in matches:
    matched_span = doc[start:end]
    print(matched_span.text)

Explanation

Lines 1 and 2: We import the necessary libraries, including spaCy and the Matcher class.

Line 4: ...