I have text which has information about the population as well the country.I would like to get the NER for the population as well as the country.
My text is as follow:
text_sent = antigens in arterial occlusive diseases in japan.using a nih standard lymphocytotoxicity test, a possible japanese specific antigen, bjw 22.2 was identified in 17 out of 48 patients with thromboangiitis obliterans (35.4 per cent), in 5 out of 15 patients with takayasu's arteritis (33.3 per cent) and in 11 out of 113 normal controls (9.7 per cent).
I have tried using this
from nltk import word_tokenize, pos_tag, ne_chunk ne_chunk(pos_tag(word_tokenize(text_sent )))
i got the tagging but didnt get any GPE tagged word.
(S antigens/NNS in/IN arterial/JJ occlusive/JJ diseases/NNS in/IN japan.using/VBG a/DT nih/JJ standard/JJ lymphocytotoxicity/NN test/NN ,/, a/DT possible/JJ japanese/JJ specific/JJ antigen/NN ,/, bjw/JJ 22.2/CD was/VBD identified/VBN in/IN 17/CD out/IN of/IN 48/CD patients/NNS with/IN thromboangiitis/NN obliterans/NNS (/( 35.4/CD per/IN cent/NN )/) ,/, in/IN 5/CD out/IN of/IN 15/CD patients/NNS with/IN takayasu/NN 's/POS arteritis/NN (/( 33.3/CD per/IN cent/NN )/) and/CC in/IN 11/CD out/IN of/IN 113/CD normal/JJ controls/NNS (/( 9.7/CD per/IN cent/NN )/) ./.)
you are not getting GPE tagged because "japan.using" is not a name of geographical location instead it Should be Japan using
I Have tried this using trained spacy model
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp(u"antigens in arterial occlusive diseases in japan.using a nih standard lymphocytotoxicity test, a possible japanese specific antigen, bjw 22.2 was identified in 17 out of 48 patients with thromboangiitis obliterans (35.4 per cent), in 5 out of 15 patients with takayasu's arteritis (33.3 per cent) and in 11 out of 113 normal controls (9.7 per cent).")
for ent in doc.ents:
print(ent.text, ent.start_char, ent.end_char, ent.label_)
#o/p
japanese 106 114 NORP
22.2 137 141 CARDINAL
17 160 162 CARDINAL
48 170 172 CARDINAL
35.4 per cent 215 228 MONEY
5 234 235 CARDINAL
15 243 245 CARDINAL
33.3 per cent 282 295 MONEY
11 304 306 CARDINAL
113 314 317 CARDINAL
9.7 per cent 335 347 MONEY
But when you modify 'japan.using' with 'Japan. using' you will get GPE tag
Japan 43 48 GPE
japanese 107 115 NORP
22.2 138 142 CARDINAL
17 161 163 CARDINAL
48 171 173 CARDINAL
35.4 per cent 216 229 MONEY
5 235 236 CARDINAL
15 244 246 CARDINAL
33.3 per cent 283 296 MONEY
11 305 307 CARDINAL
113 315 318 CARDINAL
9.7 per cent 336 348 MONEY