old_contents: from nltk.corpus import wordnet as wn
  from nltk.corpus.reader import NOUN
  from gensim.models import Word2Vec
  import os
  
  #model = Word2Vec.load_word2vec_format(os.environ['HOME'] + "/models/en_deps_300D_words.model")
  #model = Word2Vec.load_word2vec_format(os.environ['HOME'] + "/models/en_bow5_300d.model")
  model = Word2Vec.load_word2vec_format(os.environ['HOME'] + "/models/glove.42B.300d.txt", binary=False)
  
  def wv_synonyms(word):
    w = '_'.join(word.split(' '))
    print 'expanding', word
    if w in model:
      # results are not sorted, so before we truncate let's sort them,
      sorted_sims = sorted(model.most_similar(w), key=lambda tup: tup[1])
      return [x[0] for x in sorted_sims if x[1] > 0.5]
    else:
      return []
  
  for word in [ "globalization", "landscape", "abstraction", "appropriation"]:
    print word, wv_synonyms(word)
  
new_contents: from nltk.corpus import wordnet as wn
  from nltk.corpus.reader import NOUN
  from gensim.models import Word2Vec
  import os
  
  model = Word2Vec.load_word2vec_format(os.environ['HOME'] + "/models/en_deps_300D_words.model")
  #model = Word2Vec.load_word2vec_format(os.environ['HOME'] + "/models/en_bow5_300d.model")
  #model = Word2Vec.load_word2vec_format(os.environ['HOME'] + "/models/glove.42B.300d.txt", binary=False)
  #model = Word2Vec.load_word2vec_format(os.environ['HOME'] + "/models/GoogleNews-vectors-negative300.bin", binary=True)
  #model = Word2Vec.load_word2vec_format(os.environ['HOME'] + "/models/freebase-vectors-skipgram1000-en.bin", binary=True)
  
  def wv_synonyms(word):
    w = '_'.join(word.split(' '))
    if w in model:
      # results are not sorted, so before we truncate let's sort them,
      sorted_sims = sorted(model.most_similar(w), key=lambda tup: tup[1])
      return [x[0] for x in sorted_sims if x[1] > 0.5]
    else:
      return []
  
  for word in [ "pollution", "urbanization", "reclamation", "globalization", "landscape", "abstraction", "appropriation"]:
    print word, wv_synonyms(word), "\n\n"
  
subject: use this model