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 FrequencyUnitOverlayer(BaseOverlayer):
"""
A pipeline component that tags Frequency units
"""
name="frequency_unit_annotator"
dependencies = []
def __init__(self, spacy_pipeline):
self.nlp = spacy_pipeline
Token.set_extension('feature_is_frequency_indicator', default=False)
self.nlp.entity.add_label('frequency_indicator')
self.frequency_matcher = Matcher(self.nlp.vocab)
self.frequency_matcher.add('FREQUENCY_MATCHER', None,
[{'LOWER': 'bid'}],
[{'LOWER': 'prn'}],
[{'LOWER': 'qid'}],
[{'LOWER': 'tid'}],
[{'LOWER': 'qd'}],
[{'LOWER': 'daily'}],
[{'LOWER': 'hs'}],
[{'LOWER': 'as'}, {'LOWER': 'needed'}],
[{'LOWER': 'once'}, {'LOWER': 'a'}, {'LOWER': 'day'}],
[{'LOWER': 'twice'}, {'LOWER': 'a'}, {'LOWER': 'day'}]
)
def __call__(self, doc):
nlp = self.nlp
with doc.retokenize() as retokenizer:
# match and frequency indicators
matches = self.frequency_matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=nlp.vocab.strings['frequency_indicator'])
for token in span:
token._.feature_is_frequency_indicator = True
if len(span) > 1:
retokenizer.merge(span)
doc.ents = list(doc.ents) + [span]
return doc