from spacy.matcher import Matcher
from spacy.tokens import Span
from spacy.tokens import Token
from medacy.pipeline_components.feature_overlayers.base import BaseOverlayer
from medacy.pipeline_components.units.mass_unit_component import MassUnitOverlayer
from medacy.pipeline_components.units.time_unit_component import TimeUnitOverlayer
from medacy.pipeline_components.units.volume_unit_component import VolumeUnitOverlayer
[docs]class MeasurementUnitOverlayer(BaseOverlayer):
"""
A pipeline component that tags Frequency units
"""
name="measurement_unit_annotator"
dependencies = [MassUnitOverlayer, TimeUnitOverlayer, VolumeUnitOverlayer]
def __init__(self, spacy_pipeline):
self.nlp = spacy_pipeline
Token.set_extension('feature_is_measurement_unit', default=False)
self.nlp.entity.add_label('measurement_unit')
self.unit_of_measurement_matcher = Matcher(self.nlp.vocab)
self.unit_of_measurement_matcher.add('UNIT_OF_MEASUREMENT', None,
[{'ENT_TYPE': 'mass_unit'}, {'ORTH': '/'}, {'ENT_TYPE': 'volume_unit'}],
[{'ENT_TYPE': 'volume_unit'}, {'ORTH': '/'}, {'ENT_TYPE': 'time_unit'}],
[{'ENT_TYPE': 'form_unit'}, {'ORTH': '/'}, {'ENT_TYPE': 'volume_unit'}]
)
def __call__(self, doc):
nlp = self.nlp
with doc.retokenize() as retokenizer:
# match units of measurement (x/y, , etc)
matches = self.unit_of_measurement_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['measurement_unit'])
for token in span:
token._.feature_is_measurement_unit = True
if len(span) > 1:
retokenizer.merge(span)
doc.ents = list(doc.ents) + [span]
return doc