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Spelling Corrector

ruanyf Oct 16th, 2012 330 Never
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  1. import re, collections
  2.  
  3. def words(text): return re.findall('[a-z]+', text.lower())
  4.  
  5. def train(features):
  6.     model = collections.defaultdict(lambda: 1)
  7.     for f in features:
  8.         model[f] += 1
  9.     return model
  10.  
  11. NWORDS = train(words(file('big.txt').read()))
  12.  
  13. alphabet = 'abcdefghijklmnopqrstuvwxyz'
  14.  
  15. def edits1(word):
  16.    splits     = [(word[:i], word[i:]) for i in range(len(word) + 1)]
  17.    deletes    = [a + b[1:] for a, b in splits if b]
  18.    transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1]
  19.    replaces   = [a + c + b[1:] for a, b in splits for c in alphabet if b]
  20.    inserts    = [a + c + b     for a, b in splits for c in alphabet]
  21.    return set(deletes + transposes + replaces + inserts)
  22.  
  23. def known_edits2(word):
  24.     return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS)
  25.  
  26. def known(words): return set(w for w in words if w in NWORDS)
  27.  
  28. def correct(word):
  29.     candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word]
  30.     return max(candidates, key=NWORDS.get)
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