input: Write a function to create a natural language processing application in Python that extracts the top 10 most frequent words from a given text. The function should return a list of tuples where each tuple contains a word and its frequency.
  
  assert extract_top_words("Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.") == [('dolor', 1), ('amet', 1), ('consectetur', 1), ('adipiscing', 1), ('elit,', 1), ('sed', 1), ('do', 1), ('eiusmod', 1), ('tempor', 1), ('incididunt', 1)]
  assert extract_top_words("The quick brown fox jumps over the lazy dog.") == [('The', 1), ('quick', 1), ('brown', 1), ('fox', 1), ('jumps', 1), ('over', 1), ('the', 1), ('lazy', 1), ('dog.', 1)]
  assert extract_top_words("apple apple banana banana cherry cherry cherry cherry") == [('cherry', 4), ('apple', 2), ('banana', 2)]
  assert extract_top_words("") == []
output: def extract_top_words(text):
      from collections import Counter
      words = text.split()
      word_counts = Counter(words)
      top_words = word_counts.most_common(10)
      return top_words