df_medspacy_annotations = pd.DataFrame(columns=[
'Tweet_id', 'Text_section', 'Span_start', 'Span_end', 'Annotation_type',
'Extras'
])
df_medspacy_tweets_tagged = pd.DataFrame(
columns=['Tweet_id', 'Tweet_full_text'])
#------------We setup the tagger using Quick UMLS-------------------
print("Configuring the Medspacy tagger. Please wait...")
nlp = {}
#Configuring the Medspacy Tagger
nlp['default'] = en_info_3700_i2b2_2012.load()
sectionizer = Sectionizer(nlp['default'])
nlp['default'].add_pipe(sectionizer)
for index, row in df_filtered.iterrows():
annotation_default = nlp['default'](str(row['tweet_text']))
#Default model
if len(annotation_default.ents) > 0:
df_medspacy_tweets_tagged.loc[len(df_medspacy_tweets_tagged.index)] = [
row['tweet_id'], row['tweet_text']
]
for ent in annotation_default.ents:
df_medspacy_annotations.loc[len(df_medspacy_annotations)] = [
row['tweet_id'], ent, ent.start_char, ent.end_char, ent.label_,
