Fuzzy sentence matching in Python - Bommarito Consulting, LLC: http://bommaritollc.com/2014/06/fuzzy-match-sentences-in-python

is_ci_token_stopword_lemma_match.py

# ## IPython Notebook for [Bommarito Consulting](http://bommaritollc.com/) Blog Post
# ### **Link**: [Fuzzy sentence matching in Python](http://bommaritollc.com/2014/06/fuzzy-match-sentences-in-python): http://bommaritollc.com/2014/06/fuzzy-match-sentences-in-python
# **Author**: [Michael J. Bommarito II](https://www.linkedin.com/in/bommarito/)

# Imports
import nltk.corpus
import nltk.tokenize.punkt
import nltk.stem.snowball
from nltk.corpus import wordnet
import string

# Get default English stopwords and extend with punctuation
stopwords = nltk.corpus.stopwords.words('english')
stopwords.extend(string.punctuation)
stopwords.append('')

def get_wordnet_pos(pos_tag):
    if pos_tag[1].startswith('J'):
        return (pos_tag[0], wordnet.ADJ)
    elif pos_tag[1].startswith('V'):
        return (pos_tag[0], wordnet.VERB)
    elif pos_tag[1].startswith('N'):
        return (pos_tag[0], wordnet.NOUN)
    elif pos_tag[1].startswith('R'):
        return (pos_tag[0], wordnet.ADV)
    else:
        return (pos_tag[0], wordnet.NOUN)

# Create tokenizer and stemmer
tokenizer = nltk.tokenize.punkt.PunktWordTokenizer()
lemmatizer = nltk.stem.wordnet.WordNetLemmatizer()

def is_ci_token_stopword_lemma_match(a, b):
    """Check if a and b are matches."""
    pos_a = map(get_wordnet_pos, nltk.pos_tag(tokenizer.tokenize(a)))
    pos_b = map(get_wordnet_pos, nltk.pos_tag(tokenizer.tokenize(b)))
    lemmae_a = [lemmatizer.lemmatize(token.lower().strip(string.punctuation), pos) for token, pos in pos_a \
                    if token.lower().strip(string.punctuation) not in stopwords]
    lemmae_b = [lemmatizer.lemmatize(token.lower().strip(string.punctuation), pos) for token, pos in pos_b \
                    if token.lower().strip(string.punctuation) not in stopwords]

    return (lemmae_a == lemmae_b)