from spacy.matcher import Matcher
from spacy.tokens import Span
from spacy.tokens import Token
from medacy.pipeline_components.feature_overlayers.base import BaseOverlayer
[docs]class MassUnitOverlayer(BaseOverlayer):
"""
A pipeline component that tags mass units
"""
name="mass_unit_annotator"
dependencies = []
def __init__(self, spacy_pipeline):
self.nlp = spacy_pipeline
Token.set_extension('feature_is_mass_unit', default=False)
self.nlp.entity.add_label('mass_unit')
self.mass_matcher = Matcher(self.nlp.vocab)
self.mass_matcher.add('UNIT_OF_MASS', None,
[{'LOWER': 'mcg'}],
[{'LOWER': 'microgram'}],
[{'LOWER': 'micrograms'}],
[{'ORTH': 'mg'}],
[{'LOWER': 'milligram'}],
[{'LOWER': 'g'}],
[{'LOWER': 'kg'}],
[{'ORTH': 'mEq'}])
def __call__(self, doc):
nlp = self.nlp
with doc.retokenize() as retokenizer:
#match and tag mass units
matches = self.mass_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['mass_unit'])
if span is None:
raise BaseException("Span is none")
for token in span:
token._.feature_is_mass_unit = True
if len(span) > 1:
retokenizer.merge(span)
doc.ents = list(doc.ents) + [span]
return doc