input: Write a python function to extract the most probable hashtag from a given sentence using bigram collocations.
  assert get_hashtag("We are going on a picnic") == "#goingpicnic"
  assert get_hashtag("I love natural language processing") == "#naturallanguage"
  assert get_hashtag("Python programming is fun and educational") == "#pythonprogramming"
output: from nltk.collocations import BigramCollocationFinder
  from nltk.metrics import BigramAssocMeasures
  import re
  
  def get_hashtag(sentence):
      words = re.findall(r'\w+', sentence.lower())
      bigram_finder = BigramCollocationFinder.from_words(words)
      bigram_finder.apply_freq_filter(1)
      bigram_measures = BigramAssocMeasures()
      scored = bigram_finder.score_ngrams(bigram_measures.pmi)
      
      if not scored:
          return ''
      
      most_probable_bigram = max(scored, key=lambda x: x[1])[0]
      return f"#{''.join(most_probable_bigram)}"