There's a lot of ways to do this, one approach is to use token-based matching. You can use this to easily find any "tokens" in the text, like names, places, or just plain words.
Methodology
I'd recommend using Rule-based Entity Recognition in spaCy. You'll define the "rules" of what the entity looks like, here's the example from the docs where we define the following patterns to find:
- An entity type of Organization and the word Apple
- An entity type of Location and the words san and francisco
Here's that in code (live example):
from spacy.lang.en import English
from spacy.pipeline import EntityRuler
nlp = English()
ruler = EntityRuler(nlp)
# These are the rules you define, look at the docs to see what your options are.
# You don't have to use the "label", you can just look for a "pattern" if you want.
patterns = [{"label": "ORG", "pattern": "Apple"},
{"label": "GPE", "pattern": [{"LOWER": "san"}, {"LOWER": "francisco"}]}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
# The text you're searching through to find your patterns
doc = nlp("Apple is opening its first big office in San Francisco.")
# This prints out the matches
print([(ent.text, ent.label_) for ent in doc.ents])
The output of this code is: [('Apple', 'ORG'), ('San Francisco', 'GPE')]
Usage
Thankfully spaCy has some fantastic online tools for helping you write your patterns, I highly recommend you check these links out.
- Install spaCy
- Evaluate if you should use rules or a model (I suggest rules but I could be wrong), and if you should use token matcher or phrase matcher
- Read the documentation to determine how to write your patterns
- Test your patterns using the Live Rule-based Matcher Explorer
- Use the code snippet above or the code samples in the docs to see how to use your new patterns
