Annotate words based on Previous Label using SpaCy

import spacy
from spacy.pipeline import EntityRuler

nlp = spacy.load("en_core_web_sm")

ruler = EntityRuler(nlp, overwrite_ents=True)

patterns = [{"label": "MALE_NAME", "pattern": [{"ENT_TYPE": "Male"}, {"TEXT": {"REGEX": "\w+"}} ]},
            {"label": "FEMALE_NAME", "pattern": [{"ENT_TYPE": "Female"}, {"TEXT": {"REGEX": "\w+"}} ]}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)

doc = nlp("Mr. Johnson goes to Los Angeles, and Mrs. Smith went to San Francisco.")
print([(ent.text, ent.label_) for ent in doc.ents if ent.label_ in ['MALE_NAME', 'FEMALE_NAME']])

[('Mr. Johnson', 'MALE_NAME'), ('Mrs. Smith', 'FEMALE_NAME')]

import spacy
from spacy.pipeline import EntityRuler

nlp = spacy.load("en_core_web_sm")
ruler = EntityRuler(nlp, overwrite_ents=True)
patterns = [{"label": "MALE_NAME", "pattern": [{"LOWER": {"IN": ["mr", "mr."]}}, {"ENT_TYPE": "PERSON"}]},
            {"label": "FEMALE_NAME", "pattern": [{"LOWER": {"IN": ["mrs", "mrs."]}}, {"ENT_TYPE": "PERSON"}]}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)

doc = nlp("Mr. Johnson goes to Los Angeles, and Mrs. Smith went to San Francisco.")
print([(ent.text, ent.label_) for ent in doc.ents if ent.label_ in ['MALE_NAME', 'FEMALE_NAME']])

[('Mr. Johnson', 'MALE_NAME'), ('Mrs. Smith', 'FEMALE_NAME')]

import spacy
from spacy.matcher import Matcher

nlp = spacy.load("en_core_web_sm")
matcher = Matcher(nlp.vocab)

# Create patterns
male_name_pattern = [{"LOWER": {"IN": ["mr", "mr."]}}, {"ENT_TYPE": "PERSON"}]
female_name_pattern = [{"LOWER": {"IN": ["mrs", "mrs."]}}, {"ENT_TYPE": "PERSON"}]

# Add patterns
matcher.add("MALE_NAME", None, male_name_pattern)
matcher.add("FEMALE_NAME", None, female_name_pattern)

doc = nlp("Mr. Johnson goes to Los Angeles, and Mrs. Smith went to San Francisco.")
matches = matcher(doc)
for match_id, start, end in matches:
    # Get string representation of pattern name
    string_id = nlp.vocab.strings[match_id] 
    # The matched span
    span = doc[start:end]  
    print(span.text, string_id)

Mr. Johnson MALE_NAME
Mrs. Smith FEMALE_NAME