def augmentKeywordsFromSentence(self, stopWordList, tokens):
from stdwn import impl
# initialise keyword list
keywords = []
# get WordNet information for each relevant word
for token in tokens:
# get synsets
synsets = impl.lookupSynsetsByForm(token)
# go through synsets
for synset in synsets:
# append synonyms
for synonym in synset.synonyms:
keywords.append(self.underscore.sub(" ", synonym.form))
# go through hyponyms
for hyponym in synset.hyponyms():
# append synonyms
for synonym in hyponym.synonyms:
keywords.append(self.underscore.sub(" ", synonym.form))
# go through hypernyms
for hypernym in synset.relations("@"):
# append synonyms
for synonym in hypernym.synonyms:
keywords.append(self.underscore.sub(" ", synonym.form))
# build set with unique values
keywords = set(keywords)
# return keywords
return keywords
def augmentKeywordsFromSentence(self, stopWordList, tokens):
# initialise keyword list
keywords = []
# get WordNet information for each relevant word
for token in tokens:
# get synsets
synsets = impl.lookupSynsetsByForm(token)
# go through synsets
for synset in synsets:
# append synonyms
for synonym in synset.synonyms:
keywords.append(self.underscore.sub(' ', synonym.form))
# go through hyponyms
for hyponym in synset.hyponyms():
# append synonyms
for synonym in hyponym.synonyms:
keywords.append(self.underscore.sub(' ', synonym.form))
# go through hypernyms
for hypernym in synset.relations('@'):
# append synonyms
for synonym in hypernym.synonyms:
keywords.append(self.underscore.sub(' ', synonym.form))
# build set with unique values
keywords = set(keywords)
# return keywords
return keywords
# train tagging model
model = tag.Bigram()
model.train(brown.tagged([textCategories['pressReportage'], textCategories['pressEditorial'], textCategories['pressReviews'], textCategories['skillsAndHobbies'], textCategories['popularLore']]))
# tag text
text = 'I want to buy a camera'
tokens = list(tokenizer.processWhitespacesWithoutStopWords(text, 1))
taggedTokens = list(model.tag(tokens))
print tokens
print taggedTokens
# get WordNet information for each noun
for taggedToken in taggedTokens:
if taggedToken[1] == 'nn' or taggedToken[1] == None:
# get synsets
synsets = impl.lookupSynsetsByForm(taggedToken[0])
# print gloss
for synset in synsets:
# go through synonyms
print '--------------------------------------------------------------'
for synonym in synset.synonyms:
print synonym.form
# go through hyponyms
print '- Hyponyms ------------------------------------------------------------'
for hyponym in synset.hyponyms():
for hypoSynonym in hyponym.synonyms:
print synonym.form, hypoSynonym.form
# go through hypernyms
from stdwn import impl
# get synsets
synsets = impl.lookupSynsetsByForm('camera')
# print
for synset in synsets:
for item in synset:
print item
textCategories['pressReviews'], textCategories['skillsAndHobbies'],
textCategories['popularLore']
]))
# tag text
text = 'I want to buy a camera'
tokens = list(tokenizer.processWhitespacesWithoutStopWords(text, 1))
taggedTokens = list(model.tag(tokens))
print tokens
print taggedTokens
# get WordNet information for each noun
for taggedToken in taggedTokens:
if taggedToken[1] == 'nn' or taggedToken[1] == None:
# get synsets
synsets = impl.lookupSynsetsByForm(taggedToken[0])
# print gloss
for synset in synsets:
# go through synonyms
print '--------------------------------------------------------------'
for synonym in synset.synonyms:
print synonym.form
# go through hyponyms
print '- Hyponyms ------------------------------------------------------------'
for hyponym in synset.hyponyms():
for hypoSynonym in hyponym.synonyms:
print synonym.form, hypoSynonym.form
# go through hypernyms
