def unify_query(query):
"""
a peek of sorts ..
param: list of Word objs
return: synset entry from wn
"""
#build query from
query = build_wn_query(query)
print 'wordnet query: {0}'.format(query)
s = wordnet.synsets(singularize(query), pos=wordnet.NOUN)
if len(s) == 0:
#this is a bit hacky.. it's based on the assumption, if it fails, it may be a two word NN
#i.e. thrill ride fails, ride doesn't
print 'no entry for {0}..'.format(query)
s = wordnet.synsets(singularize(query.split()[1]), pos=wordnet.NOUN)
if len(s) == 0:
print 'no entry for {0}'.format(query.split()[1])
return s
def test_synset_is_physical_object(self):
from extract import synset_is_physical_object
tree_ss = wordnet.synsets("tree")[0]
self.assertTrue(synset_is_physical_object(tree_ss))
truth_ss = wordnet.synsets("truth")[0]
self.assertFalse(synset_is_physical_object(truth_ss))
def special_similarity(word):
if word.startswith("special"):
return (1.0)
return (custom_similarity(word, [
wordnet.synsets('special', pos=wordnet.ADJECTIVE)[1],
wordnet.synsets('special', pos=wordnet.ADJECTIVE)[3]
]))
def test():
word = 'walk'
pos = 'VB'
print(wordnet.synsets(word, pos))
return
hypers = get_words(word, pos, 'hyper', True, 10)
# for hyper in hypers:
# print(hyper, hypers[hyper])
print(hypers.keys())
# print('fruit' in hypers)
hypos = get_words(word, pos, 'hypo', True, 1)
# for hypo in hypos:
# print(hypo, hypos[hypo])
print(hypos.keys())
print()
print(get_synonyms('big', 'JJ'))
print(get_antonyms('big', 'JJ'))
print(get_similar('big', 'JJ'))
print()
print(get_synonyms('man', 'NN'))
print(get_antonyms('man', 'NN'))
print(get_similar('man', 'NN'))
print()
print(wordnet.synsets('go', pos='VB')[0])
print(wordnet.synsets('nice', pos='JJ')[0])
print(wordnet.synsets('well', pos='RB')[0])
print(wordnet.synsets('musical instrument', pos="NN")[0])
def number_similarity(word):
return (custom_similarity(word, [
wordnet.synsets('number')[0],
wordnet.synsets('number')[1],
wordnet.synsets('number')[4]
],
pos=wordnet.NOUN))
def alphabet_similarity(word):
return (custom_similarity(word, [
wordnet.synsets('alphabet')[0],
wordnet.synsets('character')[-2],
wordnet.synsets('letter')[1]
],
pos=wordnet.NOUN))
def hypernym_search(text, search_word):
try:
from pattern.en import wordnet
except:
print(
'Please install pattern: pip install https://github.com/clips/pattern/archive/development.zip'
)
sys.exit()
output = []
try:
for search_word in search_word.split('|'):
synset = wordnet.synsets(search_word)[0]
pos = synset.pos
possible_words = re_search(text, pos)
for match in possible_words:
word = match[0].string
synsets = wordnet.synsets(word)
if len(synsets) > 0:
hypernyms = synsets[0].hypernyms(recursive=True)
if any(search_word == h.senses[0] for h in hypernyms):
output.append(word)
except IndexError:
pass
return set(output)
def word_similarity(word1, word2): """ Similarity of 2 words as a score from 0 to 1, uses wordnet """ from pattern.en import wordnet try: a = wordnet.synsets(word1)[0] b = wordnet.synsets(word2)[0] return wordnet.similarity(a, b) except: return 0
def get_related_noun_or_not(noun, d=True): w = wordnet.synsets(noun) if w: w = w[0] w1 = w.hyponyms() w2 = w.hypernyms() if w1 + w2: nw = random.choice(w1 + w2) if nw and nw.senses: return nw.senses[0] elif wordnet.synsets(singularize(noun)) and d: return get_related_noun_or_not(singularize(noun, False)) return noun
def hypernym_search(text, search_word):
output = []
synset = wordnet.synsets(search_word)[0]
pos = synset.pos
possible_words = re_search(text, pos)
for match in possible_words:
word = match[0].string
synsets = wordnet.synsets(word)
if len(synsets) > 0:
hypernyms = synsets[0].hypernyms(recursive=True)
if any(search_word == h.senses[0] for h in hypernyms):
output.append(word)
return set(output)
def hypernym_search(text, search_word):
output = []
synset = wordnet.synsets(search_word)[0]
pos = synset.pos
possible_words = re_search(text, pos)
for match in possible_words:
word = match[0].string
synsets = wordnet.synsets(word)
if len(synsets) > 0:
hypernyms = synsets[0].hypernyms(recursive=True)
if any(search_word == h.senses[0] for h in hypernyms):
output.append(word)
return set(output)
def get_related_or_not(word, d=True, pos='NN'):
w = wordnet.synsets(word, pos=pos)
if w:
w = w[0]
w1 = w.hyponyms()
w2 = w.hypernyms()
if w1 + w2:
nw = random.choice([w] + w1 + w2)
if nw and nw.senses:
return nw.senses[0]
elif wordnet.synsets(singularize(word)) and d:
return get_related_or_not(singularize(word, False, pos))
return word
def custom_similarity(word, synsets, pos=None):
word = singularize(word.lower())
similarities = []
if pos:
word_synsets = wordnet.synsets(word, pos=pos)
else:
word_synsets = wordnet.synsets(word)
for i in synsets:
for j in word_synsets:
try:
similarities.append(wordnet.similarity(i, j))
except Exception, e:
pass
def wordnet_potential_parent(word1, pos1, word2, pos2, min_sim=0.0):
syns1 = wn.synsets(word1, pos1)
syns2 = wn.synsets(word2, pos2)
parents = []
for s1 in syns1:
for s2 in syns2:
family = wn.ancestor(s1,s2)
if family:
sim = wn.similarity(s1,s2)
if sim > min_sim:
parents.append( (family, sim) )
return parents
def custom_similarity(word, synsets, pos=None):
word = singularize(word.lower())
similarities = []
if pos:
word_synsets = wordnet.synsets(word, pos=pos)
else:
word_synsets = wordnet.synsets(word)
for i in synsets:
for j in word_synsets:
try:
similarities.append(wordnet.similarity(i, j))
except Exception, e:
pass
def lemma_is_geological_formation(lemma):
synsets = wordnet.synsets(lemma, wordnet.NOUN)
if len(synsets) > 0 and all([synset_is_proper(s) for s in synsets]):
return False
else:
return any([synset_is_geological_formation(s) for s in synsets \
if not(synset_is_proper(s))])
def find_replacement(word, pos):
shuffle(BASIC)
try:
w = next(w for w in BASIC if wordnet.synsets(w, pos=pos))
return (w, True) if random() < 0.3 else (word, False)
except:
return word, False
def synonyms(lemma, pos=NOUN):
synonyms = list()
for synset in wordnet.synsets(lemma, pos):
for synonym in synset.synonyms:
if synonym != lemma:
synonyms.append(synonym)
return synonyms
def validate(self, tag, media):
# check with wordnet
# if synset continue else return -1
# check category and decide if verify
# use google vision api to verify
# result: 1 -> good (keep image) ; 0 -> bad (discard image) ; -1 -> cannot validate (keep)
tag = singularize(tag).lower()
synset = wordnet.synsets(tag, pos=NOUN)
if not synset:
return -1
category = synset[0].lexname
if self.VALIDATE_CATEGORY == 'all':
pass
elif category in self.VALIDATE_CATEGORY:
pass
# do not return yet
else:
return -1 # not all and cannot be validated
img = requests.get(media)
gImage = google.cloud.vision.types.Image(content=img.content)
response = self.vision_client.label_detection(image=gImage)
labels = map(lambda d: d.description if d.score > 0.9 else None, response.label_annotations)
# check if tag in the detected labels with a good probability (score)
if tag in labels:
return 1
# compare synonyms
synonyms = synset[0].synonyms
# check if any synonym in labels
for synonym in synonyms:
if singularize(tag).lower() in labels:
return 1
return 0
def glossdef(token):
tokens = []
for term in token:
for token in wordnet.synsets(term):
token = token.gloss
token = tokens.append(token)
return tokens
def shift(noun):
""" Returns a (random hyponym, description)-tuple for the given noun.
"""
s = wordnet.synsets(noun)
s = s[0]
h = choice(s.hyponyms(recursive=True) or [s])
return [h.synonyms[0], h.gloss]
def shift(noun):
""" Returns a (random hyponym, description)-tuple for the given noun.
"""
s = wordnet.synsets(noun)
s = s[0]
h = choice(s.hyponyms(recursive=True) or [s])
return [h.synonyms[0], h.gloss]
def sentiment(content):
if len(wordnet.sentiment) == 0:
wordnet.sentiment.load()
relevant_types = ['JJ', 'VB', 'RB'] #adjectives, verbs, adverbs
score = 0
sentences = split(parse(content))
for sentence in sentences:
for index, word in enumerate(sentence.words):
if word.string != '' and word.type in relevant_types:
try:
synset = wordnet.synsets(word.string, word.type)
except KeyError:
#incorrect part of speech tag or not in wordnet, skip it
continue
pos, neg, obj = synset[0].weight
#weights concluding statements
#idea from [Ohana, Tierney '09]
documentpos = index / float(len(sentence.words))
#weights more subjective statements
subjscore = ((pos - neg) * (1 - obj))
score = score + subjscore * documentpos
return score
def lemma_is_natural(lemma):
synsets = wordnet.synsets(lemma, wordnet.NOUN)
if len(synsets) > 0 and all([synset_is_proper(s) for s in synsets]):
return False
else:
return any([synset_is_natural(s) for s in synsets \
if not(synset_is_proper(s))])
def get_random_word(t, ref=False):
"""Return a random word from a set filtering on lexname category if necessary"""
# If there are entries in the lexnames list for the given POS tag, limit results to that,
# otherwise just return a random word for that POS
word = None
if len(POS[t]['lexnames']):
lexname = ''
while lexname not in POS[t]['lexnames']:
word = random.choice(POS[t]['words'])[0]
synset = wordnet.synsets(get_singular(word), pos=t)
if synset:
lexname = synset[0].lexname
else:
word = random.choice(POS[t]['words'])[0]
# If required, prefix with an article
if ref:
word = referenced(word)
return word.lower()
def sentiment(content):
if len(wordnet.sentiment) == 0:
wordnet.sentiment.load()
relevant_types = ['JJ', 'VB', 'RB'] #adjectives, verbs, adverbs
score = 0
sentences = split(parse(content))
for sentence in sentences:
for index, word in enumerate(sentence.words):
if word.string != '' and word.type in relevant_types:
try:
synset = wordnet.synsets(word.string, word.type)
except KeyError:
#incorrect part of speech tag or not in wordnet, skip it
continue
pos, neg, obj = synset[0].weight
#weights concluding statements
#idea from [Ohana, Tierney '09]
documentpos = index / float(len(sentence.words))
#weights more subjective statements
subjscore = ((pos - neg) * (1 - obj))
score = score + subjscore * documentpos
return score
def list_hyponyms(word):
output = []
synsets = wordnet.synsets(word)
if len(synsets) > 0:
hyponyms = synsets[0].hyponyms(recursive=True)
output = [h.senses[0] for h in hyponyms]
return output
def list_hyponyms(word):
output = []
synsets = wordnet.synsets(word)
if len(synsets) > 0:
hyponyms = synsets[0].hyponyms(recursive=True)
output = [h.senses[0] for h in hyponyms]
return output
def expand(term, limit=3):
# EXPANSION IS ONLY DONE FOR FIRST SYNSET. WHY?
try:
hypernyms = WN.synsets(term, 'NN')[0].hypernyms(recursive=True)
return {w.senses[0] for w in hypernyms[:limit]}.union({term})
except IndexError:
return {}
def synonyms(lemma, pos=NOUN): synonyms = list() for synset in wordnet.synsets(lemma, pos): for synonym in synset.synonyms: if synonym != lemma: synonyms.append(synonym) return synonyms
def lemma_is_natural(lemma):
synsets = wordnet.synsets(lemma, wordnet.NOUN)
if len(synsets) > 0 and all([synset_is_proper(s) for s in synsets]):
return False
else:
return any([synset_is_natural(s) for s in synsets \
if not(synset_is_proper(s))])
def lemma_is_geological_formation(lemma):
synsets = wordnet.synsets(lemma, wordnet.NOUN)
if len(synsets) > 0 and all([synset_is_proper(s) for s in synsets]):
return False
else:
return any([synset_is_geological_formation(s) for s in synsets \
if not(synset_is_proper(s))])
def define_word(self, word):
synsets = wordnet.synsets(word)
if len(synsets) > 0:
gloss = synsets[0].gloss
if gloss.find(';') > -1:
gloss = gloss[:gloss.find(';')]
word = word + " (comprising of " + gloss + ") "
return word
def get_synonyms(word, pos):
""" return a set of strings, lowercase unless proper noun """
ans = set()
for ss in wordnet.synsets(word, pos):
synonyms = [synonym.replace('_', ' ') for synonym in ss.synonyms]
# print(synonyms)
ans.update(synonyms)
return ans
def wn_filter_pos(text, pos):
synsets = wordnet.synsets(text, pos=pos)
for s in synsets:
for synonym in s.synonyms:
if synonym[0].isupper():
return False
if len(synsets) > 0:
return True
def wn_filter_pos(text, pos): synsets = wordnet.synsets(text, pos=pos) for s in synsets: for synonym in s.synonyms: if synonym[0].isupper(): return False if len(synsets) > 0: return True
def define_word(self, word):
synsets = wordnet.synsets(word)
if len(synsets) > 0:
gloss = synsets[0].gloss
if gloss.find(";") > -1:
gloss = gloss[: gloss.find(";")]
word = word + " (comprising of " + gloss + ") "
return word
def WordnetFeatures(token):
synset = wordnet.synsets(token)
if len(synset) > 0:
synset = synset[0]
hypernym = synset.hypernyms(depth=2, recursive=True)
# hypernym.extend(synset.hyponyms(depth=2,recursive=True))
return [hyper.senses[0] for hyper in hypernym]
else:
return []
def lemma_is_person(lemma):
synsets = wordnet.synsets(lemma, wordnet.NOUN)
# if ALL the synsets are proper, then it's a person!
if len(synsets) > 0 and all([synset_is_proper(s) for s in synsets]):
return True
# otherwise, check ONLY the non-proper synsets
else:
return any([synset_is_person(s) for s in synsets \
if not(synset_is_proper(s))])
def WordnetFeatures(token):
synset = wordnet.synsets(token)
if len(synset) > 0:
synset = synset[0]
hypernym = synset.hypernyms(depth=2, recursive=True)
# hypernym.extend(synset.hyponyms(depth=2,recursive=True))
return [hyper.senses[0] for hyper in hypernym]
else:
return []
def lemma_is_person(lemma):
synsets = wordnet.synsets(lemma, wordnet.NOUN)
# if ALL the synsets are proper, then it's a person!
if len(synsets) > 0 and all([synset_is_proper(s) for s in synsets]):
return True
# otherwise, check ONLY the non-proper synsets
else:
return any([synset_is_person(s) for s in synsets \
if not(synset_is_proper(s))])
def max_ic(words, pos):
subj = None
syns = [wordnet.synsets(w, pos=pos) for w in words]
syns = [random.choice(s) for s in syns if s]
if syns:
vals = [(s.synonyms[0], s.ic) for s in syns]
if vals:
word, val = max(vals, key=lambda x: x[1])
subj = word
return subj
def hypernym_search(text, search_word):
try:
from pattern.en import wordnet
except:
print('Please install pattern: pip install https://github.com/clips/pattern/archive/development.zip')
sys.exit()
output = []
for search_word in search_word.split('|'):
synset = wordnet.synsets(search_word)[0]
pos = synset.pos
possible_words = re_search(text, pos)
for match in possible_words:
word = match[0].string
synsets = wordnet.synsets(word)
if len(synsets) > 0:
hypernyms = synsets[0].hypernyms(recursive=True)
if any(search_word == h.senses[0] for h in hypernyms):
output.append(word)
return set(output)
def get_similar(word, pos):
""" return a set of strings, lowercase unless proper noun """
ans = set()
for ss in wordnet.synsets(word, pos):
if ss.similar(): # a list of synsets
for ss1 in ss.similar():
similars = [
synonym.replace('_', ' ') for synonym in ss1.synonyms
]
# print(similars)
ans.update(similars)
return ans
def list_hypernyms(search_word):
try:
from pattern.en import wordnet
except:
print('Please install pattern: pip install https://github.com/clips/pattern/archive/development.zip')
sys.exit()
output = []
for synset in wordnet.synsets(search_word):
hypernyms = synset.hypernyms(recursive=True)
output.append([h.senses[0] for h in hypernyms])
return output
def list_hypernyms(search_word):
try:
from pattern.en import wordnet
except:
print('Please install pattern: pip install https://github.com/clips/pattern/archive/development.zip')
sys.exit()
output = []
for synset in wordnet.synsets(search_word):
hypernyms = synset.hypernyms(recursive=True)
output.append([h.senses[0] for h in hypernyms])
return output
def getOccurencies(dico, lis):
synlist = {}
#Loop on chapters, j is the key, dico[j] is the dictionnary of this chapter with a child "fulltext"
for j in dico:
c = dico[j]["fulltext"]
wordpos = {}
exclude = set(string.punctuation)
#c = ''.join(ch for ch in c if ch not in exclude)
#print c
#Loop on words in list
c = c.lower()
for i in range(len(c)):
for word in lis:
if c[i:len(word) + i] == word:
wordpos[i] = word
"""
for l in lis:
wlen = 0
for pos, words in enumerate(c.split()):
#print words
#wlen += len(words)
#print words
#for en, char in enumerate(c):
if l == words and l not in wordpos:
#if not l in wordpos:
wordpos[pos] = l
#break
#wordpos[l] = pos
"""
#End of word loop
#Get synonyms
for l in lis:
syns = wordnet.synsets(l)
for k in syns:
if not l in synlist:
synlist[l] = []
synlist[l].extend(k.synonyms)
#End of synonyms loop
#We update dico
dico[j]["occurencies"] = wordpos
#End of loop on chapters
setsyn = set()
lis1 = []
for word in synlist:
synlist[word] = list(set(synlist[word]))
#print(synlist)
return dico, synlist
def list_hyponyms(word):
try:
from pattern.en import wordnet
except:
print('Please install pattern: pip install https://github.com/clips/pattern/archive/development.zip')
sys.exit()
output = []
synsets = wordnet.synsets(word)
if len(synsets) > 0:
hyponyms = synsets[0].hyponyms(recursive=True)
output = [h.senses[0] for h in hyponyms]
return output
def list_hyponyms(word):
try:
from pattern.en import wordnet
except:
print('Please install pattern: pip install https://github.com/clips/pattern/archive/development.zip')
sys.exit()
output = []
synsets = wordnet.synsets(word)
if len(synsets) > 0:
hyponyms = synsets[0].hyponyms(recursive=True)
output = [h.senses[0] for h in hyponyms]
return output
def get_related(word, plural=False):
syn = wordnet.synsets(word)
if not syn:
return []
syn = syn[0]
if not syn.hypernym:
return []
fn = pluralize if plural else lambda x: x
s = sorted([(fn(' '.join(x[0].split('_'))), x.similarity(syn))
for x in syn.hypernym.hyponyms()],
key=lambda z: z[1],
reverse=True)
return [x for x in s if x[1] > 0.5 and x[1] < 1 and word not in x[0]]
def parse_phrases(documents):
for document in documents:
ptree = parsetree(document, relations=True, lemmata=True)
for sentence in ptree:
print i, sentence.string
for phrase in sentence.phrases:
for word in phrase.words:
if word.pos in WN_POS:
print i, phrase, word, word.pos, wn.synsets(word.lemma, word.pos)
else:
print i, phrase, word, word.pos
print '\n'
def all_synsets(word, pos=None):
map = {
'NOUN': wordnet.NOUN,
'VERB': wordnet.VERB,
'ADJ': wordnet.ADJECTIVE,
'ADV': wordnet.ADVERB
}
if pos is None:
pos_list = [wordnet.VERB, wordnet.ADJECTIVE, wordnet.NOUN, wordnet.ADVERB]
else:
pos_list = [map[pos]]
ret = []
for pos in pos_list:
ret.extend(wordnet.synsets(word, pos=pos))
return ret
def test_wordnet():
from pattern.en import wordnet
word = "bird"
word = "Java"
word = "C++"
word = "MongoDb"
for s in wordnet.synsets(word) :
print 'Definition:', s.gloss
print ' Synonyms:', s.synonyms
print ' Hypernyms:', s.hypernyms()
print ' Hyponyms:', s.hyponyms()
print ' Holonyms:', s.holonyms()
print ' Meronyms:', s.meronyms()
def is_animate(lemma):
# this "works" but is very eager to grant animacy even for words that have
# one synset with a person hypernym (things like "rock" count as "animate"
# because a "rock" can be a person that you depend on)
hypernyms = list()
for synset in wordnet.synsets(lemma, pos=NOUN):
# skip synsets that are proper nouns, as these are always animate!
if any([s[0].isupper() for s in synset.synonyms]):
continue
synonyms = list()
for s in synset.hypernyms(recursive=True):
synonyms.extend(s.synonyms)
if 'person' in synonyms:
return True
return False
def is_animate(lemma): # this "works" but is very eager to grant animacy even for words that have # one synset with a person hypernym (things like "rock" count as "animate" # because a "rock" can be a person that you depend on) hypernyms = list() for synset in wordnet.synsets(lemma, pos=NOUN): # skip synsets that are proper nouns, as these are always animate! if any([s[0].isupper() for s in synset.synonyms]): continue synonyms = list() for s in synset.hypernyms(recursive=True): synonyms.extend(s.synonyms) if 'person' in synonyms: return True return False
def get_words(word, pos, hyper_hypo, recursive=False, depth=None):
""" return a dict { str.lower : zipf_freq.log }
if depth = NOne, depth = 1
"""
ans = {} # a dictioinary
# find out which synset for the word
for ss in wordnet.synsets(word, pos):
# if ss.pos.startswith(pos.upper()): # this is what we want!
if hyper_hypo == 'hyper':
for x in ss.hypernyms(recursive, depth):
ans.update({syn.replace('_', ' ') : zipf_frequency(syn.replace('_', ' '), 'en') \
for syn in x.synonyms})
elif hyper_hypo == 'hypo':
for x in ss.hyponyms(recursive, depth):
ans.update({syn.replace('_', ' ') : zipf_frequency(syn.replace('_', ' '), 'en') \
for syn in x.synonyms})
# break # only use the first synset, assuming it's the most common one
return ans
def get_alternations(word, pos, synset_id, nlp, verbose=False):
""" return all alternations """
alternations = {} # {word_str : simi_score}
# find all alternations of walk
for ss in wordnet.synsets(word, pos):
if int(ss.id) != int(synset_id): continue
for hyper in ss.hypernyms():
for hypo in hyper.hyponyms():
if hypo.pos != pos: continue # only want the same pos
for synonym in hypo.synonyms:
if synonym.lower() == word.lower():
continue # don't want ss here
if synonym not in alternations:
simi_score = nlp(word).similarity(nlp(synonym))
if simi_score > 0:
# print(word, synonym, simi_score)
alternations[synonym] = simi_score
if verbose: print('found {} alternations'.format(len(alternations)))
return alternations
def get_tables(words):
"""Build a list of tables for the SQL statement from random words"""
# http://wordnet.princeton.edu/man/lexnames.5WN.html
lexnames = ['noun.plant',
'noun.animal',
'noun.food',
'noun.shape',
'noun.body',
'noun.artifact',
'noun.object'
]
# Loop until we find a table name that is less than the MAX_TABLE_NAME_LENGTH and has a
# noun category that is in the list of lexnames above
tables = []
for i in range(0, MAX_TABLES):
lexname = ''
while lexname not in lexnames:
((word, tag), f) = random.choice(words)
word = word.lower()
s = wordnet.synsets(word)
if len(s):
s = s[0]
lexname = s.lexname
if len(s.hypernym) > MAX_TABLE_NAME_LENGTH:
lexname = ''
else:
lexname = ''
tables.append(s)
print '------------------------------------------------------------------------'
print word
print tables[0].hyponyms()
print '------------------------------------------------------------------------'
return tables
def add_vocab(self,word):
if word in self.synsets:
self.synsets[word]+=1 #count frequency
if word in self.vocab:
self.total_words+=1
else:
self.vocab[word] = word
self.total_words+=1
pass
if word in self.vocab:
self.total_words+= 1
pass
elif not word in self.synsets:
singular = singularize(word)
if not wordnet.synsets(singular): singular = word
self.vocab[word] = singular
self.total_words+= 1
elif word in self.synsets:
self.vocab[word] = word
self.total_words+= 1