from __future__ import unicode_literals

import spacy.en
from spacy.en import English

nlp = English()
doc = nlp('text', tag=True, parse=True)

text = '"But there is no telling what the hurricane damage will be," Anderson said, in reference to Hurricane Fran which swept through North and South Carolina last week. Some industry sources said the hurricane may have brought beneficial rains while others predicted losses and damage to open bolls. "Fran changes the picture considerably but it is too soon (to determine losses)," said Jarral Neeper, an analyst with Calcot Ltd. He pegged the estimate at 18.717 million bales. Smith Barney analyst David Brandon compared the effect of Hurricane Fran to that of Hurricane Hugo in 1989, which he said buoyed yields in North and South Carolina.'

doc = nlp(text, tag=True)

for ent in doc.ents:
    if len(ent.orth_.split()) > 1:
      start = text.index(ent.orth_)
      end = start+len(ent.orth_)
      print ent.orth_ + ' start: ' + str(start) + ' ' + 'end: ' + str(end) + ' ' + 'entity: ' + ent.label_
      doc.merge(start, end, '', '', ent.label_)
      for token in doc:    
          print token.orth_