python
15 lines · 1 tab
Dr. Elena Vasquez
Apr 2026
1 tab
import spacy
from spacy.matcher import Matcher
nlp = spacy.load('en_core_web_sm')
matcher = Matcher(nlp.vocab)
matcher.add('INCIDENT_ID', [[{'TEXT': {'REGEX': '^INC-[0-9]{6}$'}}]])
text = 'Customer referenced INC-102301 and requested refund after a payment failure.'
doc = nlp(text)
entities = [(ent.text, ent.label_) for ent in doc.ents]
matches = [doc[start:end].text for _, start, end in matcher(doc)]
print(entities)
print(matches)
1 file · python
Explain with highlit
I like spaCy for production NLP because it balances performance, ergonomics, and deployability. It is especially good for entity extraction, rule-based matching, and clean token-level processing. I often pair learned models with explicit match patterns when the domain has stable language conventions.
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