text: #!/usr/bin/env python -*- coding: utf-8 -*-
  #
  # Python Word Sense Disambiguation (pyWSD): WSD all-words tests
  #
  # Copyright (C) 2014-2015 alvations
  # URL:
  # For license information, see LICENSE.md
  
  from string import punctuation
  
  from nltk import word_tokenize
  from nltk.corpus import wordnet as wn
  from nltk.corpus import brown, stopwords
  
  
  from pywsd.lesk import simple_lesk, original_lesk, cosine_lesk, adapted_lesk
  from pywsd.similarity import max_similarity
  from pywsd.utils import lemmatize
  from pywsd.allwords_wsd import disambiguate
  
  print "======== TESTING all-words lesk ===========\n"
  for sentence in brown.sents()[:10]:
      # Retrieves a tokenized text from brown corpus.
      sentence = " ".join(sentence)
      # Annotate the full sentence.
      print disambiguate(sentence, simple_lesk, prefersNone=True, keepLemmas=True)
      print disambiguate(sentence, original_lesk)
      print disambiguate(sentence, adapted_lesk, keepLemmas=True)
      print disambiguate(sentence, cosine_lesk, prefersNone=True)
      print
  print
  
  print "======== TESTING all-words path maxsim ===========\n"
  print "This is going to take some time, have some coffee...\n"
  for sentence in brown.sents()[0:1]:
      # Retrieves a tokenized text from brown corpus.
      sentence = " ".join(sentence)
      # Annotate the full sentence.
      print disambiguate(sentence, max_similarity, similarity_option='path')
      print disambiguate(sentence, max_similarity, similarity_option='wup')
  print
  
  print "======== TESTING all-words info content maxsim ==========="
  print "===This is going to take some time, have some coffee...===\n"
  for sentence in brown.sents()[0:1]:
      # Retrieves a tokenized text from brown corpus.
      sentence = " ".join(sentence)
      # Annotate the full sentence.
      print disambiguate(sentence, max_similarity, similarity_option='lch')
      print disambiguate(sentence, max_similarity, similarity_option='res')
      print disambiguate(sentence, max_similarity, similarity_option='jcn')
      print disambiguate(sentence, max_similarity, similarity_option='lin')
  
   
  ''' 
  # TODO: do proper doctests...
  
  # Lemma options and None when no Synset.
  >>> disambiguate('I went to the bank to deposit my money')
  [('I', '#STOPWORD/PUNCTUATION#'), ('went', Synset('go.v.28')), ('to', '#STOPWORD/PUNCTUATION#'), ('the', '#STOPWORD/PUNCTUATION#'), ('bank', Synset('depository_financial_institution.n.01')), ('to', '#STOPWORD/PUNCTUATION#'), ('deposit', Synset('deposit.n.04')), ('my', '#STOPWORD/PUNCTUATION#'), ('money', Synset('money.n.03'))]
  >>> disambiguate('I went to the bank to deposit my money', prefersNone=True, keepLemmas=True)
  [('I', 'i', None), ('went', 'went', Synset('go.v.28')), ('to', 'to', None), ('the', 'the', None), ('bank', 'bank', Synset('depository_financial_institution.n.01')), ('to', 'to', None), ('deposit', 'deposit', Synset('deposit.n.04')), ('my', 'my', None), ('money', 'money', Synset('money.n.03'))]
  # Using alternative algorithms.
  >>> disambiguate('I went to the bank to deposit my money', algorithm=cosine_lesk, prefersNone=True)
  [('I', None), ('went', Synset('travel.v.01')), ('to', None), ('the', None), ('bank', Synset('bank.v.05')), ('to', None), ('deposit', Synset('down_payment.n.01')), ('my', None), ('money', Synset('money.n.01'))]
  >>> disambiguate('I went to the bank to deposit my money', algorithm=maxsim, similarity_option='wup', prefersNone=True)
  [('I', None), ('went', Synset('sound.v.02')), ('to', None), ('the', None), ('bank', Synset('deposit.v.02')), ('to', None), ('deposit', Synset('deposit.v.02')), ('my', None), ('money', Synset('money.n.01'))]
  '''
   