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from nltk import regexp_tokenize
def tokenize(term):
# Adapted From Natural Language Processing with Python
regex = r'''(?xi)
(?:H|S)\.\ ?(?:(?:J|R)\.\ )?(?:Con\.\ )?(?:Res\.\ )?\d+ # Bills
| ([A-Z]\.)+ # Abbreviations (U.S.A., etc.)
| ([A-Z]+\&[A-Z]+) # Internal ampersands (AT&T, etc.)
| (Mr\.|Dr\.|Mrs\.|Ms\.) # Mr., Mrs., etc.
| \d*\.\d+ # Numbers with decimal points.
| \d\d?:\d\d # Times.
| \$?[,\.0-9]+\d # Numbers with thousands separators, (incl currency).
| (((a|A)|(p|P))\.(m|M)\.) # a.m., p.m., A.M., P.M.
| \w+((-|')\w+)* # Words with optional internal hyphens.
| \$?\d+(\.\d+)?%? # Currency and percentages.
| (?<=\b)\.\.\.(?=\b) # Ellipses surrounded by word borders
| [][.,;"'?():-_`]
# Strip punctuation from this one; solr doesn't know about any of it
tokens = regexp_tokenize(term, regex)
# tokens = [re.sub(r'[.,?!]', '', token) for token in tokens] # instead of this we just test word length
return tokens
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