def test_word_similarity():
from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()
dog = wns.word2synset('dog')
cat = wns.word2synset('cat')
# Measuring semantic similarity between concepts using Path method
assert wns.similarity(dog[0], cat[0], 'path') is not None # 0.2
# Computing English word similarity using Li method
assert wns.word_similarity('dog', 'cat',
'li') is not None # 0.449327301063
# Computing Spanish word similarity using Lin method
assert wns.monol_word_similarity('perro', 'gato', 'spa',
'lin') is not None #0.876800984373
# Computing Chinese word similarity using Wu & Palmer method
assert wns.monol_word_similarity('狗', '猫', 'cmn',
'wup') is not None # 0.857142857143
# Computing Spanish and English word similarity using Resnik method
assert wns.crossl_word_similarity('perro', 'cat', 'spa', 'eng',
'res') is not None #7.91166650904
# Computing Spanish and Chinese word similarity using Jiang & Conrad method
assert wns.crossl_word_similarity('perro', '猫', 'spa', 'cmn',
'jcn') is not None #0.31023804699
# Computing Chinese and English word similarity using WPath method
assert wns.crossl_word_similarity('狗', 'cat', 'cmn', 'eng',
'wpath') is not None #0.593666388463
def controlledSetWordNetSimilarity(self, word, similarWords):
wns = WordNetSimilarity()
for similarWord in similarWords.copy():
if wns.word_similarity(
word, similarWord, 'li'
) < 0.9996: # Variable to control accuracy of controlset
similarWords.discard(similarWord)
return similarWords
def test_classification_evaluation():
from sematch.evaluation import AspectEvaluation
from sematch.application import SimClassifier, SimSVMClassifier
from sematch.semantic.similarity import WordNetSimilarity
evaluation = AspectEvaluation()
X, y = evaluation.load_dataset()
wns = WordNetSimilarity()
word_sim = lambda x, y: wns.word_similarity(x, y)
simclassifier = SimClassifier.train(zip(X, y), word_sim)
evaluation.evaluate(X, y, simclassifier)
simSVMclassifier = SimSVMClassifier.train(X, y, word_sim)
evaluation.evaluate(X, y, simSVMclassifier)
def test_wordsim_evaluation():
from sematch.evaluation import WordSimEvaluation
from sematch.semantic.similarity import WordNetSimilarity
wordsim_eval = WordSimEvaluation()
wns = WordNetSimilarity()
#define similarity metrics
lin = lambda x, y: wns.word_similarity(x, y, 'lin')
wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)
#evaluate similarity metrics
assert wordsim_eval.evaluate_multiple_metrics({'lin':lin, 'wpath':wpath}, 'noun_simlex') is not None
#performa Steiger's Z significance Test
assert wordsim_eval.statistical_test('wpath', 'lin', 'noun_simlex') is not None
def semantic_matching(trend_one, trend_two):
treshold = 0.3
trend_one_processed = text_processing(trend_one, keep_spaces=True)
trend_two_processed = text_processing(trend_two, keep_spaces=True)
# The options are Wordnet, YAGO and DBpedia (only the first seems usable)
wns = WordNetSimilarity()
matches = list({
x['original']
for x in trend_one_processed for y in trend_two_processed
if wns.word_similarity(x['processed'], y['processed'], 'li') > treshold
})
if len(matches) == 0: return 'No matches'
return matches
def test_wordsim_evaluation():
from sematch.evaluation import WordSimEvaluation
from sematch.semantic.similarity import WordNetSimilarity
wordsim_eval = WordSimEvaluation()
wns = WordNetSimilarity()
#define similarity metrics
lin = lambda x, y: wns.word_similarity(x, y, 'lin')
wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)
#evaluate similarity metrics
assert wordsim_eval.evaluate_multiple_metrics({
'lin': lin,
'wpath': wpath
}, 'noun_simlex') is not None
#performa Steiger's Z significance Test
assert wordsim_eval.statistical_test('wpath', 'lin',
'noun_simlex') is not None
def test_query_ned():
from sematch.nlp import FeatureExtractor
from sematch.nlp import EntityFeature
from sematch.nlp import SpaCyNLP
from sematch.utility import FileIO
from sematch.semantic.relatedness import TextRelatedness
from sematch.nel import EntityDisambiguation
import itertools
sy = SpaCyNLP()
features = EntityFeature.load(
feature_dict_file='models/query_features.json')
extractor = FeatureExtractor(features, sy.pos_tag)
ned = EntityDisambiguation(extractor)
rel = TextRelatedness()
from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()
#print wns.word_similarity('cooling', 'air_conditioner', 'li')
#similarity = lambda x,y : rel.text_similarity(x,y, model='lsa')
query = FileIO.read_json_file('dataset/ned/query_ned_cleaned.txt')
query = [q for q in query if extractor.context_features(q['query'])]
print len(query)
import warnings
warnings.filterwarnings("ignore")
metrics = ['path', 'wup', 'res', 'lin', 'jcn', 'wpath']
for m in metrics:
print m
similarity = lambda x, y: wns.word_similarity(x, y, m)
for k in range(1, 21):
gold = []
predict = []
for q in query:
gold.append(q['gold'])
#e = ned.text_disambiguate(q['query'], q['candidate'], similarity)
e = ned.word_disambiguate(q['query'],
q['candidate'],
similarity,
K=k)
predict.append(e)
from sklearn.metrics import precision_recall_fscore_support
#from sklearn.metrics import classification_report
#print classification_report(gold, predict)
print precision_recall_fscore_support(gold,
predict,
average='weighted')[2]
def test_wordnet_similarity():
from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()
dog = wns.word2synset('dog')
cat = wns.word2synset('cat')
# Measuring semantic similarity between concepts using Path method
assert wns.similarity(dog[0], cat[0], 'path') is not None # 0.2
# Computing English word similarity using Li method
assert wns.word_similarity('dog', 'cat', 'li') is not None# 0.449327301063
# Computing Spanish word similarity using Lin method
assert wns.monol_word_similarity('perro', 'gato', 'spa', 'lin') is not None#0.876800984373
# Computing Chinese word similarity using Wu & Palmer method
assert wns.monol_word_similarity('狗', '猫', 'cmn', 'wup') is not None# 0.857142857143
# Computing Spanish and English word similarity using Resnik method
assert wns.crossl_word_similarity('perro', 'cat', 'spa', 'eng', 'res') is not None#7.91166650904
# Computing Spanish and Chinese word similarity using Jiang & Conrad method
assert wns.crossl_word_similarity('perro', '猫', 'spa', 'cmn', 'jcn') is not None#0.31023804699
# Computing Chinese and English word similarity using WPath method
assert wns.crossl_word_similarity('狗', 'cat', 'cmn', 'eng', 'wpath') is not None#0.593666388463
def test_simcat_classifier():
from sematch.classification import SimCatClassifier
from sematch.evaluation import ABSAEvaluation
from sematch.semantic.similarity import WordNetSimilarity
# defining similarity metric
wns = WordNetSimilarity()
sim_metric_jcn = lambda x, y: wns.word_similarity(x, y, 'jcn')
sim_metric_wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.9)
# loadding dataset
absa_eval = ABSAEvaluation()
X_train_16, y_train_16 = absa_eval.load_dataset('eval/aspect/ABSA16_Restaurants_Train_SB1_v2.xml')
X_test_16, y_test_16 = absa_eval.load_dataset('eval/aspect/ABSA16_Restaurants_Train_SB1_v2.xml')
# train the classifiers
sim_jcn_classifier = SimCatClassifier.train(zip(X_train_16, y_train_16), sim_metric_jcn)
sim_wpath_classifier = SimCatClassifier.train(zip(X_train_16, y_train_16), sim_metric_wpath)
# evaluate the classifiers
#absa_eval.evaluate(X_test_16, y_test_16, sim_jcn_classifier)
#absa_eval.evaluate(X_test_16, y_test_16, sim_wpath_classifier)
assert sim_jcn_classifier is not None
assert sim_wpath_classifier is not None
def test_query_ned():
from sematch.nlp import FeatureExtractor
from sematch.nlp import EntityFeature
from sematch.nlp import SpaCyNLP
from sematch.utility import FileIO
from sematch.semantic.relatedness import TextRelatedness
from sematch.nel import EntityDisambiguation
import itertools
sy = SpaCyNLP()
features = EntityFeature.load(feature_dict_file='models/query_features.json')
extractor = FeatureExtractor(features, sy.pos_tag)
ned = EntityDisambiguation(extractor)
rel = TextRelatedness()
from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()
#print wns.word_similarity('cooling', 'air_conditioner', 'li')
#similarity = lambda x,y : rel.text_similarity(x,y, model='lsa')
query = FileIO.read_json_file('dataset/ned/query_ned_cleaned.txt')
query = [q for q in query if extractor.context_features(q['query'])]
print len(query)
import warnings
warnings.filterwarnings("ignore")
metrics = ['path', 'wup', 'res', 'lin', 'jcn', 'wpath']
for m in metrics:
print m
similarity = lambda x, y: wns.word_similarity(x, y, m)
for k in range(1, 21):
gold = []
predict = []
for q in query:
gold.append(q['gold'])
#e = ned.text_disambiguate(q['query'], q['candidate'], similarity)
e = ned.word_disambiguate(q['query'], q['candidate'], similarity, K=k)
predict.append(e)
from sklearn.metrics import precision_recall_fscore_support
#from sklearn.metrics import classification_report
#print classification_report(gold, predict)
print precision_recall_fscore_support(gold, predict, average='weighted')[2]
# pip install sematch
# nltk.download('wordnet_ic')
# You also need to edit one of the sematch library files, sparql in case you are using python 3. You need to change the print statement.
from sematch.semantic.similarity import WordNetSimilarity
import pandas as pd
wns = WordNetSimilarity()
words = ['artist', 'musician', 'scientist', 'physicist', 'actor', 'movie']
sim_matrix = [[wns.word_similarity(w1, w2, 'wpath') for w1 in words]
for w2 in words]
df = pd.DataFrame(sim_matrix, index=words, columns=words)
print(df)
print(wns.word_similarity("Dog", "Cat"))
class fmodel(object):
def __init__(self):
self.out = {}
self.keras = keras_similar()
self.classifier = Qclassifier()
self.spell=Spelling()
self.wn = WordNetSimilarity()
self.en_nlp = spacy.load("en_core_web_md")
self.stopwords_en=[]
with open(os.path.join(os.path.dirname(os.path.realpath(__file__)),
'utils', 'stopwords_en.txt')) as f:
self.stopwords_en = f.read().splitlines()
def ent_nltk(self, sentence):
ne_tree = ne_chunk(pos_tag(word_tokenize(sentence)))
iob_tagged = tree2conlltags(ne_tree)
ents = [[0, 0, 10]]
for i in range(len(iob_tagged)):
each = iob_tagged[i]
if each[2] != 'O':
if ents[-1][2] == (i - 1):
ents[-1][0] += " " + each[0]
ents[-1][2] = i
else:
ents.append([each[0], each[2][2:], i])
if len(ents) > 1:
ents = ents[1:]
ents = [ent[0] for ent in ents]
else:
ents = []
return ents
def mini_similar(self, q1, q2):
self.out = {'sim': 0, 'sim_per': 0.0, 'keras': 0, 'class': ["", ""],
'f_class': 0, "sentiment": [0, 0, 0],
"keywords": [[""], [""]], "numbers": [[], []],
"entities": [[], []], "max_keywords": 0, "keywords_sim": 0}
regex = re.compile('[^a-zA-Z0-9]')
q1 = regex.sub('', q1)
q2 = regex.sub('', q2)
if q1 == q2:
self.out['sim'] = 1
self.out['sim_per'] = 100
return self.out
else:
s1 = self.wn.word_similarity(q1, q2, 'lin')
print(s1)
if s1 > 0.9:
self.out['sim'] = 1
self.out['sim_per'] = 100
return self.out
elif s1 > 0.8:
self.out['sim'] = 1
self.out['sim_per'] = s1 # max([s1,s2,s3])
return self.out
return self.out
def is_one_word(self, q1, q2):
l1 = q1
l2 = q2
flag1 = False
flag2 = False
stop = True
word1 = ""
word2 = ""
if len(l1)!=len(l2):
return False
else:
for i in range(len(l1)):
if l1[i].text != l2[i].text or l1[i].lemma_ != l2[i].lemma_:
if(flag2):
return False
elif l1[i].text in self.stopwords_en and l2[i].text in self.stopwords_en:
word1 = l1[i].text
word2 = l2[i].text
flag1 = True
else:
word1 = l1[i].lemma_
word2 = l2[i].lemma_
flag1 = True
flag2 = True
if flag1:
self.out = self.mini_similar(word1,word2)
return True
def similar(self, text, challenge):
if not isinstance(text, str) or not isinstance(challenge, str):
q1 = text
q2 = challenge
else:
q1 = normalizr.normalize(text, normalizations)
q2 = normalizr.normalize(challenge, normalizations)
q1 = self.spell.correct_str(q1,True)
q2 = self.spell.correct_str(q2,True)
if (len(q1.split()) == 1 and len(q2.split()) == 1) or (q1 == q2):
return self.mini_similar(q1, q2)
regex = re.compile(u'/') # [^a-zA-Z]')
q1 = regex.sub('', q1)
q2 = regex.sub('', q2)
self.out = {'sim': 0, 'sim_per': 0.0, 'keras': 0.0, 'class': ["", ""],
'f_class': 0, "sentiment": [0, 0, 0],
"keywords": [[""], [""]], "numbers": [[], []],
"entities": [[], []], "max_keywords": 0,
"keywords_sim": 0.0}
q1_neg_list = list(set(mark_negation(q1.split())[0]))
q2_neg_list = list(set(mark_negation(q2.split())[0]))
if q1 == "" or q2 == "":
return self.out
sq1 = self.en_nlp(q1)
sq2 = self.en_nlp(q2)
if self.is_one_word(sq1, sq2):
return self.out
count = 0
start_time = time.time()
entsq1 = self.ent_nltk(q1)
entsq2 = self.ent_nltk(q2)
self.out['entities'][1] = entsq2
self.out['entities'][0] = entsq1
for ent in sq1.ents:
if ent.text not in entsq1:
# self.out['entities'][0].append([ent.label_, ent.text])
self.out['entities'][0].append(ent.text)
for ent in sq2.ents:
if ent.text not in entsq2:
# self.out['entities'][1].append((ent.label_, ent.text))
self.out['entities'][1].append(ent.text)
if self.out['entities'][0]:
if self.out['entities'][1]:
if(len(self.out['entities'][0])!= len(self.out['entities'][1])):
return self.out
self.out['max_keywords'] += len(
set(self.out['entities'][0] + self.out['entities'][1]))
for each in self.out['entities'][0]:
if(each in self.out['entities'][1]):
count += 1
else:
return self.out
else:
return self.out
elif self.out['entities'][1]:
return self.out
elapsed_time = time.time() - start_time
self.out['keras'] = self.keras.similar(q1, q2)
self.out['sentiment'][0] = get_sentiment_values(q1)[1]['compound']
self.out['sentiment'][1] = get_sentiment_values(q2)[1]['compound']
self.out['sentiment'][2] = abs(
self.out['sentiment'][0] - self.out['sentiment'][1])
if (abs(self.out['sentiment'][0]) > 0.3 and abs(
self.out['sentiment'][1]) > 0.3):
if self.out['sentiment'][2] >= 0.6:
return self.out
start_time = time.time()
self.out['class'][0] = self.classifier.classify_question(sq1)
self.out['class'][1] = self.classifier.classify_question(sq2)
self.out['f_class'] = (self.out['class'][0] == self.out['class'][1])
self.out['keywords'][0], self.out['numbers'][0] = extract_features(sq1)
self.out['keywords'][1], self.out['numbers'][1] = extract_features(sq2)
self.out['max_keywords'] += len(
set(self.out['keywords'][0] + self.out['keywords'][1]))
if self.out['class'][0] > 0 and self.out['class'][1] > 0:
self.out['max_keywords'] += 1
for each in self.out['keywords'][0]:
if each in self.out['keywords'][1]:
if (each in q1_neg_list and each not in q2_neg_list) or (
each in q2_neg_list and each not in q1_neg_list):
self.out['max_keywords'] += 1
else:
if(each in self.stopwords_en):
count += 0.30
#self.out['max_keywords'] -= 1
else:
count+=1
if self.out['numbers'][0]:
self.out['max_keywords'] += 1
if self.out['numbers'][1]:
self.out['max_keywords'] += 1
if self.out['numbers'][1] != self.out['numbers'][0]:
return self.out
elif self.out['numbers'][1]:
self.out['max_keywords'] += 1
if self.out['class'][0] > 0 and self.out['class'][1] > 0:
self.out['max_keywords'] += 1
if self.out['f_class']:
if self.out['max_keywords'] > 1:
count += 1
else:
count += 0.35
# keywords_s1= [x for x in keywords_s1 if x not in keywords_s2]
# keywords_s3= [x for x in keywords_s2 if x not in keywords_s1]
if self.out['max_keywords'] < 1:
self.out['keywords_sim'] = 0
else:
self.out['keywords_sim'] = (count / self.out['max_keywords']) * 100
self.out['sim_per'] = (self.out['keywords_sim']+self.out['keras'])/2.0
#print(self.out['keywords_sim'],count,self.out['max_keywords'])
'''
k_value = []
s_value = []
k = 100.0
s = 30.0
k_step = 10.0
s_step = 4.0
self.out["sim_per"] = (self.out['keywords_sim'] + self.out['keras']) / 2
for i in range(7):
k -= k_step
s += s_step
k_value.append(k)
s_value.append(s)
'''
s_value = [34.0, 40.0, 50.0, 55.0, 60.0, 60.0, 60.0]
k_value = [90.0, 85.0, 80.0, 75.0, 70.0, 60.0, 30.0]
if self.out['keras'] >= k_value[0]:
if self.out['keywords_sim'] >= s_value[0]:
self.out['sim'] = 1
return self.out
elif self.out['keras'] > k_value[1]:
if self.out['keywords_sim'] >= s_value[1]:
self.out['sim'] = 1
return self.out
elif self.out['keras'] > k_value[2]:
if self.out['keywords_sim'] >= s_value[2]:
self.out['sim'] = 1
return self.out
elif self.out['keras'] > k_value[3]:
if self.out['keywords_sim'] >= s_value[3]:
self.out['sim'] = 1
return self.out
elif self.out['keras'] > k_value[4]:
if self.out['keywords_sim'] >= s_value[4]:
self.out['sim'] = 1
return self.out
elif self.out['keras'] > k_value[5]:
if self.out['keywords_sim'] >= s_value[5]:
self.out['sim'] = 1
return self.out
elif self.out['keras'] > k_value[6]:
if self.out['keywords_sim'] >= s_value[6]:
self.out['sim'] = 1
return self.out
return self.out
def similarr(self, text, questions=list()):
answer, max_similarity = None, 0
if not text or len(questions) == 0:
return answer, max_similarity
for question in questions:
try:
result = self.similar(text.lower(),
question.get('question').lower())
except:
result = self.similar(text, question.get('question'))
if result.get('sim') == 1:
confidence = result.get('sim_per')
if max_similarity <= confidence <= 100:
max_similarity = confidence
answer = question.get('id')
# print("round stop\n")
if max_similarity >= 95:
break
# print('[Stop]')
return answer, max_similarity
def get_suggestions(self, text=None, texts=list()):
res = []
s = []
min_confidence = 45
for each in texts:
result = self.similar(text, each.get('question').lower())
if result.get('sim') == 1:
confidence = result.get('sim_per')
if 100 >= confidence > min_confidence:
if each.get('rich_text'):
response = each.get('rich_text')
else:
flow = int(each.get('response').replace('flow-', ''))
flow = Flow.objects.filter(id=flow).values('id', 'name',
'category__name')
if flow.exists():
response = [{'flow': flow}]
else:
response = None
if response:
res.append((confidence, each.get('id'), response,
each.get('question')))
s = sorted(res, key=operator.itemgetter(0), reverse=True)[:3]
suggestions = []
for e in s:
if e[2]:
messages = []
for m in e[2]:
messages.append({'message': format_message(m)})
suggestions.append({'confidence': e[0], 'id': e[1],
'message': messages})
return suggestions
class TextPreprocessor(BaseEstimator, TransformerMixin):
"""
Transform input text into feature representation
"""
def __init__(self,
corpus,
feature_num=10,
model='onehot',
wn_method='path',
vec_file='models/GoogleNews-vectors-negative300.bin',
binary=True):
"""
:param corpus: use a corpus to train a vector representation
:param feature_num: number of dimensions
:param model: onehot or wordnet or word2vec or both
"""
self._model = model
self._wn_method = wn_method
self._features = self.extract_features(corpus, feature_num)
self._wns = WordNetSimilarity(
) if model == 'wordnet' or model == 'both' else None
self._wvs = WordVecSimilarity(
vec_file,
binary) if model == 'word2vec' or model == 'both' else None
def fit(self, X, y=None):
return self
def inverse_transform(self, X):
return X
def extract_features(self, corpus, feature_num=10):
cat_word = {}
for sent, cat in corpus:
cat_word.setdefault(cat,
[]).extend(lemmatization(word_tokenize(sent)))
features = {cat: Counter(cat_word[cat]) for cat in cat_word}
feature_words = []
for c, f in features.iteritems():
words, counts = zip(*f.most_common(feature_num))
feature_words.extend(list(words))
feature_words = set(feature_words)
return feature_words
def similarity(self, tokens, feature, method='wordnet'):
if method == 'wordnet':
sim = lambda x: self._wns.word_similarity(feature, x, self.
_wn_method)
else:
sim = lambda x: self._wvs.word_similarity(feature, x)
return max(map(sim, tokens) + [0.0])
def unigram_features(self, tokens):
words = set(tokens)
features = {}
for f in self._features:
features['contains({})'.format(f)] = (f in words)
return features
def wordnet_features(self, tokens):
words = set(tokens)
features = {}
for f in self._features:
features['wns({})'.format(f)] = self.similarity(words, f)
return features
def word2vec_features(self, tokens):
words = set(tokens)
features = {}
for f in self._features:
features['w2v({})'.format(f)] = self.similarity(words,
f,
method='word2vec')
return features
def semantic_features(self, tokens):
words = set(tokens)
features = {}
for f in self._features:
features['wns({})'.format(f)] = self.similarity(words, f)
features['w2v({})'.format(f)] = self.similarity(words,
f,
method='word2vec')
return features
def transform(self, X):
tokenize = lambda x: lemmatization(word_tokenize(x))
X_tokens = map(tokenize, X)
if self._model == 'onehot':
return map(self.unigram_features, X_tokens)
elif self._model == 'wordnet':
return map(self.wordnet_features, X_tokens)
elif self._model == 'word2vec':
return map(self.word2vec_features, X_tokens)
elif self._model == 'both':
return map(self.semantic_features, X_tokens)
from sematch.semantic.similarity import WordNetSimilarity
import codecs
wns = WordNetSimilarity()
poems = codecs.open('generatedpoems.txt', 'r', encoding='utf-8')
data = open('data.txt', 'a')
for x in poems:
temp_words = x.split(" ")
total = 0
count = 0
for y in range(len(temp_words) - 1):
total += wns.word_similarity(temp_words[y], temp_words[y + 1], 'li')
count += 1
total /= count
data.write(str(total) + '\n')
data.close()
poems.close()
#print wns.word_similarity(w1, w2, 'li')
from sematch.semantic.similarity import WordNetSimilarity
L1=[]
L2=[]
L3=[]
wns = WordNetSimilarity()
# Computing English word similarity using Li method
x=wns.word_similarity('programmer', 'coder', 'software engineer')
if(x>0.7):
L1.append('programmer')
L1.append('coder')
L1.append('software engineer')
else:continue
# Computing english word similarity using Li method
wns.word_similarity('softwrae program', 'computer software', 'software system')
if(x>0.7):
L1.append('software program')
L1.append('computer software')
L1.append('software system')
else:continue
def yhmh_nlp(url, trigger_words):
text, triggers = parse_my_url(url, trigger_words)
print("triggers2: %s" % (triggers))
if text is "" or len(triggers) == 0:
return ""
client = language.LanguageServiceClient()
if isinstance(text, six.binary_type):
text = text.decode('utf-8')
# Instantiates a plain text document.
document = types.Document(content=text,
type=enums.Document.Type.PLAIN_TEXT)
# Detects entities in the document. You can also analyze HTML with:
# document.type == enums.Document.Type.HTML
entities = client.analyze_entities(document).entities
verbose = True
counter = 0
counter2 = 0
text_output_array = pd.DataFrame(np.zeros((len(entities), 3)))
for entity in entities:
entity_type = enums.Entity.Type(entity.type)
if len(entity.name) < 25 and '.' not in entity.name:
text_output_array.iloc[counter, 0] = entity.name
text_output_array.iloc[counter, 1] = entity_type.name
text_output_array.iloc[counter, 2] = entity.salience
counter += 1
else:
counter2 += 1
celebrity_status = 0
if len(entities) > 0:
if entities[0].metadata.get(
'wikipedia_url',
'-') != '-' and text_output_array.iloc[0, 1] == 'PERSON':
celebrity_status = 1
elif entities[1].metadata.get(
'wikipedia_url',
'-') and text_output_array.iloc[1, 1] == 'PERSON':
celebrity_status = 1
else:
celebrity_status = 0
text_output_array = text_output_array.iloc[0:len(entities) - counter2, :]
# Detects the sentiment of the text
#sentiment = client.analyze_sentiment(document=document).document_sentiment
wns = WordNetSimilarity()
keywords_target = pd.Series.to_list(text_output_array[0])
#keywords_target = list(set(keywords_target))
#seen = set(keywords_target)
#keywords_target = []
#for x in keywords_target:
# if x not in seen:
# keywords_target.append(x)
# seen.add(x)
#
#keywords_target=seen
forbidden_keywords = [
'medicine', 'drug', 'fun', 'hospital', 'suicide', 'death', 'mental',
'health', 'illness', 'insta', ',man', 'woman', 'family', 'people',
'many', 'place', 'same', 'others', 'brain', 'all', 'end', 'statement',
'lot', 'condolences'
]
regex = re.compile(r'([A-Z]([a-z])+)')
selected_files = list(filter(regex.search, keywords_target))
res = list(set(keywords_target) - set(selected_files))
regex = re.compile(r'^@')
selected_files = list(filter(regex.search, res))
res = list(set(keywords_target) - set(selected_files))
regex = re.compile(r"\b[A-Z][A-Z]+\b")
selected_files = list(filter(regex.search, res))
res = list(set(res) - set(selected_files))
regex = re.compile(r'([A-Z]([a-z])+)')
selected_files = list(filter(regex.search, res))
res = list(set(res) - set(selected_files))
for key in range(len(res)):
if ' ' in res[key]:
res[key] = res[key].split(' ')[0]
for x in range(len(res)):
for y in range(len(forbidden_keywords)):
if res[x] == forbidden_keywords[y]:
res[x] = []
res = list(filter(None, res))
res_dictionary = Counter(res)
res_output = res_dictionary.most_common(10)
res_output = dict(res_output)
res_output = list(res_output.keys())
print(res_output)
res = res_output[0:num_keywords]
database = pd.read_csv(
CURATED_LIST
) #('/Users/vmutai/Projects/HMH/admin/microblog/app/yhmh_curated_articles.csv')
if celebrity_status == 1:
database = database[database.celebrity == 1]
elif celebrity_status == 0:
database = database[database.celebrity == 0]
similarity_ranks = pd.DataFrame(np.zeros(database.shape[0]))
for z in range(database.shape[0]):
newlist = []
N_rows = len(res)
keywords_source = database.iloc[z, 4:4 + num_keywords]
keywords_source = pd.Series.tolist(keywords_source)
N_cols = len(keywords_source)
#similarity_list = pd.DataFrame(np.zeros((N_rows, N_cols)))
foo = [1]
for x in range(len(res)):
for y in range(len(keywords_source)):
value = wns.word_similarity(res[x], keywords_source[y], 'lin')
#similarity_matrix.at[x,y]=value
foo.append(value)
matrix_average = sum(foo) / np.count_nonzero(foo)
similarity_ranks.at[z, 0] = matrix_average
maximum = pd.DataFrame.idxmax(similarity_ranks)
url_to_return = pd.Series.tolist(database.iloc[maximum, 0])
print(url_to_return)
title = pd.Series.tolist(database.iloc[maximum, 1])
def output(title, res_output, url_to_return):
a = {
'header': title[0],
'keywords_list': res_output,
'url_recommendation': url_to_return[0]
}
print("JSON DUMP")
print(a)
try:
return json.dumps(a)
except:
return "awesome2!"
json_output = output(title, res_output, url_to_return)
print(json_output)
return json_output
from nltk.corpus import wordnet_ic
from nltk.corpus.reader.wordnet import information_content
brown_ic = wordnet_ic.ic('ic-brown.dat')
wns = WordNetSimilarity()
# arg1 and arg2: predicates represented in strings separated by underscores
# e.g. cast_member or star
preA = sys.argv[1].split("_")
preB = sys.argv[2].split("_")
# arg3: pairwise similarity matrix in which rows are separated by underscore
# e.g. 0.6_0.5, or 0.6,0.7_0.3,0.4
data = []
for a in preA:
row = []
for b in preB:
wdsim = wns.word_similarity(a, b, 'wup')
row.append(wdsim)
data.append(row)
data = numpy.matrix(data)
#max values in rows
Amax = data.max(1)
icA = []
for i in range(len(preA)):
try:
if lesk(preA, preA[i]) is None:
# preA is not in WordNet
icA.append(1)
elif information_content(lesk(preA, preA[i]), brown_ic) == 'inf':
icA.append(1)
else:
icA.append(information_content(lesk(preA, preA[i]), brown_ic))
matriceListe = []
matricelistePaire = []
matricelistePaireSort = []
matricelistePaireAction = []
matricelistePaireObject = []
for word in sorted_terms_lists:
tokens = word
for index, row in listcara.iterrows():
abstractNumber = 'abs'.format(str((i)))
listaction = row['Colonne3']
listaction = re.sub(r'\([^)]*\)', '', listaction)
#comparaison betwen tags and classe Triz
indiceSimAction = wns.word_similarity(word, str(listaction))
if indiceSimAction == 0 or word.isdigit() == True:
#print "rien a faire "
continue
else:
valeurs = []
valeurs = [
i, NumberBrevet, word, listaction, indiceSimAction,
abstract, urlEspacenet
]
ligne = ",".join(str(v) for v in valeurs) + "\n"
clean.append(t)
return clean
cleanCleanCat1 = cleanTexts(categoryList1)
cleanCleanCat2 = cleanTexts(categoryList2)
wns = WordNetSimilarity()
similarCategories = []
for cat in cleanCleanCat1:
sims = []
for t in cleanCleanCat2:
TextSim = []
for w in cat:
# wdsSim=[1 if w == wr else wns.word_similarity(w, wr, 'li') for wr in t]
wdsSim = [wns.word_similarity(w, wr, 'li') for wr in t]
TextSim.extend(wdsSim)
sims.append((cleanCleanCat2.index(t), sum(TextSim)))
if max(sims, key=lambda x: x[1])[1] > 0:
similarCategories.append(
(max(sims, key=lambda x: x[1])[0], max(sims,
key=lambda x: x[1])[1]))
else:
similarCategories.append('')
print('{0} texts out of {1} done'.format(
cleanCleanCat1.index(cat) + 1, len(cleanCleanCat1)))
with open('S:/path/In-market audiences_sim.csv',
'w',
newline='',
encoding='utf-8') as csvfile:
from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()
# Computing English word similarity using Li method
wns.word_similarity('dog', 'cat', 'li') # 0.449327301063
# Computing Spanish word similarity using Lin method
wns.monol_word_similarity('perro', 'gato', 'spa', 'lin') #0.876800984373
# Computing Chinese word similarity using Wu & Palmer method
wns.monol_word_similarity('狗', '猫', 'cmn', 'wup') # 0.857142857143
# Computing Spanish and English word similarity using Resnik method
wns.crossl_word_similarity('perro', 'cat', 'spa', 'eng', 'res') #7.91166650904
# Computing Spanish and Chinese word similarity using Jiang & Conrad method
wns.crossl_word_similarity('perro', '猫', 'spa', 'cmn', 'jcn') #0.31023804699
# Computing Chinese and English word similarity using WPath method
wns.crossl_word_similarity('狗', 'cat', 'cmn', 'eng', 'wpath') #0.593666388463
