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from nltk import metrics, stem, tokenize
 
stemmer = stem.PorterStemmer()
 
def normalize(s):
    words = tokenize.wordpunct_tokenize(s.lower().strip())
    return ' '.join([stemmer.stem(w) for w in words])
 
def fuzzy_match(s1, s2, max_dist=3):
    return metrics.edit_distance(normalize(s1), normalize(s2)) <= max_dist


s=""
while s.lower()!="bye":
	s=raw_input("You: ")
	s_array=s.split(";")
	if len(s_array)>1:
		s1=s_array[0].strip()
		s2=s_array[1].strip()
		print fuzzy_match(s1,s2)