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- import os
- from nltk.tag import StanfordNERTagger
- os.environ['CLASSPATH'] = 'stanford-ner-2015-12-09/stanford-ner.jar'
- os.environ['STANFORD_MODELS'] = 'stanford-ner-2015-12-09/classifiers'
- inp_str = 'total revenue received was one hundred and twenty five percent 125% for last financial year'
- split_inp_str = inp_str.split()
- st = StanfordNERTagger('english.muc.7class.distsim.crf.ser.gz')
- print(st.tag(split_inp_str))
- [('total', 'O'), ('revenue', 'O'), ('received', 'O'), ('was', 'O'), ('one', 'O'), ('hundred', 'O'), ('and', 'O'), ('twenty', 'O'), ('five', 'PERCENT'), ('percent', 'PERCENT'), ('125%', 'O'), ('for', 'O'), ('last', 'O'), ('financial', 'O'), ('year', 'O')]
- [('total', 'O'), ('revenue', 'O'), ('received', 'O'), ('was', 'O'), ('one', 'PERCENT'), ('hundred', 'PERCENT'), ('and', 'PERCENT'), ('twenty', 'PERCENT'), ('five', 'PERCENT'), ('percent', 'PERCENT'),('125%', 'PERCENT'), ('for', 'O'), ('last', 'O'), ('financial', 'O'), ('year', 'O')]
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