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Determine if two (already lemmatized) words are similar or not.
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def sim(word1, word2, lch_threshold=2.15, verbose=False): | |
"""Determine if two (already lemmatized) words are similar or not. | |
Call with verbose=True to print the WordNet senses from each word | |
that are considered similar. | |
The documentation for the NLTK WordNet Interface is available here: | |
http://nltk.googlecode.com/svn/trunk/doc/howto/wordnet.html | |
""" | |
from nltk.corpus import wordnet as wn | |
results = [] | |
for net1 in wn.synsets(word1): | |
for net2 in wn.synsets(word2): | |
try: | |
lch = net1.lch_similarity(net2) | |
except: | |
continue | |
# The value to compare the LCH to was found empirically. | |
# (The value is very application dependent. Experiment!) | |
if lch >= lch_threshold: | |
results.append((net1, net2)) | |
if not results: | |
return False | |
if verbose: | |
for net1, net2 in results: | |
print net1 | |
print net1.definition | |
print net2 | |
print net2.definition | |
print 'path similarity:' | |
print net1.path_similarity(net2) | |
print 'lch similarity:' | |
print net1.lch_similarity(net2) | |
print 'wup similarity:' | |
print net1.wup_similarity(net2) | |
print '-' * 79 | |
return True |
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@bwbaugh It should be
net1.definition()
. It's now a method.