ner_output = [('Remaking', 'O'), ('The', 'O'), ('Republican',
    'ORGANIZATION'), ('Party', 'ORGANIZATION')]
chunked, pos = [], ''
for i, word_pos in enumerate(ner_output):
    word, pos = word_pos
if pos in ['PERSON', 'ORGANIZATION', 'LOCATION'] and pos == prev_tag:
    chunked[-1] += word_pos
else:
    chunked.append(word_pos)
prev_tag = pos
clean_chunked = [(tuple([' '.join(wordpos[::2]), wordpos[-1]]) if len(
    wordpos) != 2 else wordpos) for wordpos in chunked]
print(clean_chunked)
Разделение выходных данных Stanford Named Entity Recognizer (NER) из формата NLTK