def create_graphs(doc_list):
documents = doc_list
if documents is None:
documents = default_document_list()
distance_functions = [
(wn.lch_similarity(SYNSETS[0], SYNSETS[0]), 'lch', lambda sense_1, sense_2: wn.lch_similarity(sense_1, sense_2)),
(1.0, 'lin', lambda sense_1, sense_2: wn.lin_similarity(sense_1, sense_2, CORPUS)),
(10.636958516573292, 'res', lambda sense_1, sense_2: wn.res_similarity(sense_1, sense_2, CORPUS)),
(wn.jcn_similarity(SYNSETS[0], SYNSETS[0], CORPUS), 'jcn', lambda sense_1, sense_2: wn.jcn_similarity(sense_1, sense_2, CORPUS)),
(1.0, 'path', lambda sense_1, sense_2: wn.path_similarity(sense_1, sense_2)),
]
all_senses = []
for doc in documents:
for sense in doc.top_senses():
all_senses.append((sense, doc.name))
against_colors = ['r', 'b', 'g']
against_to = [wn.synset(word) for word in ["economy.n.01", "philosophy.n.02", "politics.n.01"]]
create_against_graph('phyl_eco_pol', documents, all_senses, against_to, distance_functions, against_colors)
against_to = SYNSETS
against_colors = [(random(), random(), random()) for _i in range(0, len(SYNSETS))]
create_against_graph('handpicked', documents, all_senses, against_to, distance_functions, against_colors)
create_graph_top_senses(documents, all_senses, distance_functions)
def test_path_similarities(self):
from nltk.corpus import wordnet as nltk_wn
nltk_cat = nltk_wn.synset('cat.n.1')
nltk_dog = nltk_wn.synset('dog.n.1')
nltk_bus = nltk_wn.synset('bus.n.1')
our_cat = our_wn.synset('cat.n.1')
our_dog = our_wn.synset('dog.n.1')
our_bus = our_wn.synset('bus.n.1')
assert nltk_wn.path_similarity(nltk_cat,
nltk_dog) == our_wn.path_similarity(
our_cat, our_dog)
assert nltk_wn.wup_similarity(nltk_cat,
nltk_dog) == our_wn.wup_similarity(
our_cat, our_dog)
assert nltk_wn.lch_similarity(nltk_cat,
nltk_dog) == our_wn.lch_similarity(
our_cat, our_dog)
assert nltk_wn.path_similarity(nltk_cat,
nltk_bus) == our_wn.path_similarity(
our_cat, our_bus)
assert nltk_wn.wup_similarity(nltk_cat,
nltk_bus) == our_wn.wup_similarity(
our_cat, our_bus)
assert nltk_wn.lch_similarity(nltk_cat,
nltk_bus) == our_wn.lch_similarity(
our_cat, our_bus)
def get_synset_similarity(first_sentence: str, second_sentence: str, method: str):
sentence_tagged_1 = pos_tag(first_sentence)
sentence_tagged_2 = pos_tag(second_sentence)
sentence_tagged_wn_1 = get_sentences_tagged_with_wn_and_cleaned(sentence_tagged_1)
sentence_tagged_wn_2 = get_sentences_tagged_with_wn_and_cleaned(sentence_tagged_2)
synsets = {}
synsets, key_list1 = get_synset_tag(sentence_tagged_wn_1, synsets)
synsets, key_list2 = get_synset_tag(sentence_tagged_wn_2, synsets)
synsets_combinations = list(product(key_list1, key_list2))
resulting_similarity = []
for first_word, second_word in synsets_combinations:
if first_word == second_word:
resulting_similarity.append(1)
continue
first_synset = synsets[first_word][0]
second_synset = synsets[second_word][0]
first_tag = synsets[first_word][1]
second_tag = synsets[second_word][1]
if method == "path":
path_sim = first_synset.path_similarity(second_synset)
if path_sim is None:
resulting_similarity.append(0)
else:
resulting_similarity.append(path_sim)
if method == "lch":
if first_tag == second_tag:
lch_sim = wn.lch_similarity(first_synset, second_synset)
if lch_sim is None:
resulting_similarity.append(0)
else:
lch_norm = lch_sim / wn.lch_similarity(first_synset, first_synset)
resulting_similarity.append(lch_norm)
if method == "wup":
wup_sim = first_synset.wup_similarity(second_synset)
if wup_sim is None:
resulting_similarity.append(0)
else:
resulting_similarity.append(wup_sim)
if method == "lin":
if first_tag == second_tag and first_tag in ['n', 'v']:
lin_sim = first_synset.lin_similarity(second_synset, brown_ic)
if lin_sim is None:
resulting_similarity.append(0)
else:
resulting_similarity.append(lin_sim)
if not resulting_similarity:
return 0
else:
return sum(resulting_similarity) / len(resulting_similarity)
def most_similar_lch(synsets_dict, verb):
best_similarity = -1
most_similar = str()
verb_synset = wn.synsets(verb, pos=wn.VERB)[0]
for verb, synset in synsets_dict.items():
if wn.lch_similarity(synset, verb_synset) > best_similarity:
best_similarity = wn.lch_similarity(synset, verb_synset)
most_similar = verb
return most_similar
def compare_allsynsets(method, word1, word2):
ss1 = wordnet.synsets(word1)
ss2 = wordnet.synsets(word2)
simi, simi_value = 0.0, 0.0
for (s1, s2) in product(ss1, ss2):
# if SYNpos and s1.pos() != s2.pos(): # SYN-POS
# continue
# if TWpos and s1.pos() != pos: # Target word POS
# continue
if method == "PATH":
simi = s1.path_similarity(s2)
elif method == "LCH":
simi = wordnet.lch_similarity(s1, s2)
elif method == "WUP":
simi = wordnet.wup_similarity(s1, s2)
elif method == "RES":
simi = wordnet.res_similarity(s1, s2, brown_ic)
elif method == "JCN":
if s1.pos() == s2.pos() and s1.pos() in ['n', 'a', 'v'
]: # can't do diff POS
simi = wordnet.jcn_similarity(s1, s2, brown_ic)
elif method == "LIN":
if s1.pos() == s2.pos() and s1.pos() in ['n', 'a', 'v'
]: # can't do diff POS
simi = wordnet.lin_similarity(s1, s2, brown_ic)
else:
sys.exit("Error! No similarity methods!")
if simi > simi_value:
simi_value = simi
return simi_value
def similarityWordNet(word1, word2):
"""
Similarity between two words with nltk
Input: word1, word2 (String)
Return: similarity (float)
"""
#print (word1,"-",word2)
word1 = str(wn.morphy(word1))
word2 = str(wn.morphy(word2))
palabras = wn.synsets(word1)
#print (palabras)
if len(palabras) == 0:
print("no existe")
return False
temp = str(palabras[0])
temp = temp[8:-2]
#print (">",temp)
word1 = wn.synset(str(temp))
#print (word1)
palabras = wn.synsets(word2)
#print (palabras)
if len(palabras) == 0:
print("no existe")
return False
temp = str(palabras[0])
temp = temp[8:-2]
#print (">",temp)
word2 = wn.synset(str(temp))
#print (word2)
"""
Return a score denoting how similar two word senses are,
based on the shortest path that connects the senses in the is-a
(hypernym/hypnoym) taxonomy. The score is in the range 0 to 1.
"""
#similarity1 = word1.path_similarity(word2)
#similarity1 = wn.path_similarity(word1, word2)
try:
#print (wn.path_similarity(word1, word2))
#if (wn.path_similarity(word1, word2) > 0.5): #(hypernym/hypnoym) taxonomy
print(wn.wup_similarity(word1, word2))
if (wn.wup_similarity(word1, word2) > 0.5): #Wu-Palmer Similarity
return True
except:
return False
"""
Leacock-Chodorow Similarity: Return a score denoting how similar
two word senses are, based on the shortest path that connects
the senses (as above) and the maximum depth of the taxonomy in
which the senses occur. range 3.6
"""
similarity2 = wn.lch_similarity(word1, word2)
"""
Wu-Palmer Similarity: Return a score denoting how similar
two word senses are, based on the depth of the two senses in
the taxonomy and that of their Least Common Subsumer (most specific ancestor node).
range 0.92
"""
similarity3 = wn.wup_similarity(word1, word2)
def get_best_synset_pair(word_1, word_2, pos_1=POS_SET, pos_2=POS_SET):
"""
Choose the pair with highest path similarity among all pairs.
Mimics pattern-seeking behavior of humans.
"""
#synsets_1 = wn.synsets(word_1)
synsets_1 = [s for s in wn.synsets(word_1) if s.pos() in pos_1]
#synsets_2 = wn.synsets(word_2)
synsets_2 = [s for s in wn.synsets(word_2) if s.pos() in pos_2]
max_sim = None
best_pair = None, None
for synset_1 in synsets_1:
for synset_2 in synsets_2:
if synset_1.pos() == synset_2.pos():
#sim = wn.path_similarity(synset_1, synset_2)
sim = wn.lch_similarity(synset_1, synset_2) # same POS needed
if (max_sim == None) or (max_sim < sim):
max_sim = sim
best_pair = synset_1, synset_2
#if best_pair!=(None,None): # or max_sim!=None
if max_sim != None:
spd = best_pair[0].shortest_path_distance(best_pair[1])
lch = best_pair[0].lowest_common_hypernyms(best_pair[1])
lch_depth = None
if lch:
lch_depth = max(s.min_depth() for s in lch)
return best_pair, max_sim, spd, lch_depth
return None
def __init__(self, metric="path", double_aggregator=False):
"""
:param metric: path lch and wup metric
:param double_aggregator:
"""
self.metric = metric
self.aggregation_mode_prev = ['max', 'mean',
'median'] #["mean", "max", "median"]
self.aggregation_mode = ["mean", "std", "max", "min", "median"]
self.aggregator = [
None if m == "" else getattr(np, m) for m in self.aggregation_mode
]
self.aggregator_prev = [
None if m == "" else getattr(np, m)
for m in self.aggregation_mode_prev
]
self.double_aggregator = double_aggregator
if self.metric == "path": # scene shortest path
self.metric_func = lambda syn1, syn2: wn.path_similarity(
syn1, syn2)
elif self.metric == "lch":
self.metric_func = lambda syn1, syn2: wn.lch_similarity(syn1, syn2)
elif self.metric == "wup": # words' depth and ancestor depth + shortest path
self.metric_func = lambda syn1, syn2: wn.wup_similarity(syn1, syn2)
else:
raise (ValueError(
"Wrong similarity metric: %s, should be one of path/lch/wup." %
self.metric))
def get_lch_average(self, sentence1, sentence2):
sentence1_unique, sentence2_unique = self.sentence_difference(
sentence1, sentence2)
avg_similarity = 0
total_count = 0
# Measure similarity for each unique word from A to each unique word to B
for sentence1_word in sentence1_unique:
for sentence2_word in sentence2_unique:
sentence1_word_tag = sentence1.get_tag(sentence1_word)
sentence2_word_tag = sentence2.get_tag(sentence2_word)
synsets_word1 = wordnet.synsets(sentence1_word,
sentence1_word_tag)
synsets_word2 = wordnet.synsets(sentence2_word,
sentence2_word_tag)
if len(synsets_word1) == 0:
synsets_word1 = wordnet.synsets(sentence1_word)
if len(synsets_word2) == 0:
synsets_word2 = wordnet.synsets(sentence2_word)
if len(synsets_word1) > 0 and len(synsets_word2) > 0:
# Skip words with different tags
if synsets_word1[0].pos() != synsets_word2[0].pos():
continue
similarity = wordnet.lch_similarity(
synsets_word1[0], synsets_word2[0])
if similarity != None:
avg_similarity += similarity
total_count += 1
if total_count == 0:
return 0
return float(avg_similarity) / float(total_count)
def get_lch_min(self, sentence1, sentence2):
sentence1_unique, sentence2_unique = self.sentence_difference(
sentence1, sentence2)
min_similarity = maxint
# Measure similarity for each unique word from A to each unique word to B
for sentence1_word in sentence1_unique:
for sentence2_word in sentence2_unique:
sentence1_word_tag = sentence1.get_tag(sentence1_word)
sentence2_word_tag = sentence2.get_tag(sentence2_word)
synsets_word1 = wordnet.synsets(sentence1_word,
sentence1_word_tag)
synsets_word2 = wordnet.synsets(sentence2_word,
sentence2_word_tag)
if len(synsets_word1) == 0:
synsets_word1 = wordnet.synsets(sentence1_word)
if len(synsets_word2) == 0:
synsets_word2 = wordnet.synsets(sentence2_word)
if len(synsets_word1) > 0 and len(synsets_word2) > 0:
# Skip words with different tags
if synsets_word1[0].pos() != synsets_word2[0].pos():
continue
similarity = wordnet.lch_similarity(
synsets_word1[0], synsets_word2[0])
if similarity != None:
min_similarity = min(similarity, min_similarity)
if min_similarity == maxint:
return 0
return min_similarity
def _get_simil_term(self, x, y, mode='lch'):
'''
Returns the similarity between two terms x and y
Args:
x, y (str)
mode = lch | path | wup
'''
w1 = wn.synsets(x)
w2 = wn.synsets(y)
if len(w1) == 0 or len(w2) == 0:
return 0
else:
if mode == 'lch':
return max([
wn.lch_similarity(e1, e2) for e1 in w1 for e2 in w2
if e1.pos == e2
])
elif mode == 'path':
return max([
wn.path_similarity(e1, e2) for e1 in w1 for e2 in w2
if e1.pos == e2
])
elif mode == 'wup':
return max([
wn.wup_similarity(e1, e2) for e1 in w1 for e2 in w2
if e1.pos == e2
])
def relaxedSimi(syn1, syn2):
"""
Compute similarity between two synsets
"""
try:
return wn.lch_similarity(syn1, syn2) or 0
except WordNetError:
return 0
def lch(self, synset_a, synset_b):
return (
self.normalize(
self.MAX_VALUE,
wordnet.lch_similarity(synset_a, synset_b, verbose=True),
)
if synset_a.pos() == synset_b.pos()
else 0
)
def classify(self, ex):
word = ex["word"].value
synset_ex = wn.synsets(word.replace(" ", "_"))[0]
similarities = [
wn.lch_similarity(synset_ex, synset_t)
for synset_t in self.training_synsets
]
cls_i = na.argmax(similarities)
return self.training_table[cls_i]["class"]
def similarity_by_path(sense1, sense2, option="path"):
if option.lower() in ["path", "path_similarity"]: # Path similaritys
return max(wn.path_similarity(sense1,sense2),
wn.path_similarity(sense1,sense2))
elif option.lower() in ["wup", "wupa", "wu-palmer", "wu-palmer"]: # Wu-Palmer
return wn.wup_similarity(sense1, sense2)
elif option.lower() in ['lch', "leacock-chordorow"]: # Leacock-Chodorow
if sense1.pos != sense2.pos: # lch can't do diff POS
return 0
return wn.lch_similarity(sense1, sense2)
def wnsensesim(synset1, synset2, metric):
if metric == 'path_similarity':
return wn.path_similarity(synset1, synset2)
elif metric == 'lch_similarity':
return wn.lch_similarity(synset1, synset2)
elif metric == 'wup_similarity':
return wn.wup_similarity(synset1, synset2)
else:#add more similarity measures e.g., jcn
print "Unsupported wn similarity measure requested"
def similarity_by_path(sense1, sense2, option="path"):
""" Returns maximum path similarity between two senses. """
if option.lower() in ["path", "path_similarity"]: # Path similaritys
return max(wn.path_similarity(sense1,sense2),
wn.path_similarity(sense1,sense2))
elif option.lower() in ["wup", "wupa", "wu-palmer", "wu-palmer"]: # Wu-Palmer
return wn.wup_similarity(sense1, sense2)
elif option.lower() in ['lch', "leacock-chordorow"]: # Leacock-Chodorow
if sense1.pos != sense2.pos: # lch can't do diff POS
return 0
return wn.lch_similarity(sense1, sense2)
def __init__(self, obs_corpus, target_corpus, metric="path", aggregation_mode_prev="", aggregation_mode=""):
super().__init__(obs_corpus, target_corpus, aggregation_mode, None, aggregation_mode_prev)
self.metric = metric
if self.metric == "path":
self.metric_func = lambda syn1, syn2: wn.path_similarity(syn1, syn2)
elif self.metric == "lch":
self.metric_func = lambda syn1, syn2: wn.lch_similarity(syn1, syn2)
elif self.metric == "wup":
self.metric_func = lambda syn1, syn2: wn.wup_similarity(syn1, syn2)
else:
raise(ValueError("Wrong similarity metric: %s, should be one of path/lch/wup."%self.metric))
def __init__(self, obs_corpus, target_corpus, metric="path", aggregation_mode_prev="", aggregation_mode=""):
super().__init__(obs_corpus, target_corpus, aggregation_mode, None, aggregation_mode_prev)
self.metric = metric
if self.metric == "path":
self.metric_func = lambda syn1, syn2: wn.path_similarity(syn1, syn2)
elif self.metric == "lch":
self.metric_func = lambda syn1, syn2: wn.lch_similarity(syn1, syn2)
elif self.metric == "wup":
self.metric_func = lambda syn1, syn2: wn.wup_similarity(syn1, syn2)
else:
raise(ValueError("Wrong similarity metric: %s, should be one of path/lch/wup."%self.metric))
def compute_similarities(s1, s2, sim):
if sim == "path":
return wn.path_similarity(s1, s2)
elif sim == "lch":
return wn.lch_similarity(s1, s2)
elif sim == "wup":
return wn.wup_similarity(s1, s2)
elif sim == "res":
return wn.res_similarity(s1, s2, genesis_ic)
elif sim == "jcn":
return wn.jcn_similarity(s1, s2, genesis_ic)
elif sim == "lin":
return wn.lin_similarity(s1, s2, genesis_ic)
def similarity_by_path(sense1, sense2, option="path"):
""" Returns maximum path similarity between two senses. """
if option.lower() in ["path", "path_similarity"]: # Path similaritys
return max(wn.path_similarity(sense1,sense2),
wn.path_similarity(sense1,sense2))
elif option.lower() in ["wup", "wupa", "wu-palmer", "wu-palmer"]: # Wu-Palmer
return wn.wup_similarity(sense1, sense2)
elif option.lower() in ['lch', "leacock-chordorow"]: # Leacock-Chodorow
if sense1.pos != sense2.pos: # lch can't do diff POS
return 0
return wn.lch_similarity(sense1, sense2)
return wn.lin_similarity(sense1, sense2, wnic.ic('ic-bnc-add1.dat'))
def bagSimilarity(s1, s2) :
if(len(s1) == 0 or len(s2) == 0) :
return 1
total = 0;
for a in s1:
for b in s2:
if(a.pos() == b.pos()) :
total += wn.lch_similarity(a,b)
total /= (len(s1)*len(s2))
return total
def dist_all_synsets(first, second):
f_syns = wn.synsets(first)
s_syns = wn.synsets(second)
#Path SImilarity
#A 0-1 similarity score based on the shortest path that connects the senses in the is-a (hypernym/hypnoym) taxonomy.
#A score of 1 represents identity i.e. comparing a sense with itself will return 1.
least_sim = 0.0
try:
for f in f_syns:
for s in s_syns:
path_sim = wn.path_similarity(f, s)
if path_sim > least_sim:
least_sim = path_sim
except:
pass
#Leacock-Chodorow Similarity
#A similarity score of the shortest path connecting the senses & the maximum depth of the taxonomy in which the senses occur.
#The relationship is given as -log(p/2d) where p is the shortest path length and d the taxonomy depth.
max_lch = 0.0
for f in f_syns:
for s in s_syns:
lch = 0.0
try:
lch = wn.lch_similarity(s, f)
except WordNetError:
pass
if lch > max_lch:
max_lch = lch
max_lch = max_lch / 3.6375
#Wu-Palmer Similarity
#A similarity score based on the depth of the two senses in the taxonomy and that of their Least Common Subsumer (most specific ancestor node).
#The LCS does not necessarily feature in the shortest path connecting the two senses, as it is by definition the common ancestor deepest in the taxonomy, not closest to the two senses. Typically, however, it will so feature. Where multiple candidates for the LCS exist, that whose shortest path to the root node is the longest will be selected. Where the LCS has multiple paths to the root, the longer path is used for the purposes of the calculation.
wup_sim = 0
try:
wup_sim = wn.wup_similarity(f_syns[0], s_syns[0])
if (wup_sim == None):
wup_sim = -1
except:
pass
return (least_sim, max_lch, wup_sim)
def checksim(self, synset1, synset2):
score = 0
for syn1 in synset1:
for syn2 in synset2:
try:
ns = wn.lch_similarity(syn1, syn2)
except:
ns = 0
# ns = wn.wup_similarity(syn1,syn2)
if isinstance(ns, float):
if ns > score:
score = ns
return (score)
def wnsim(synset1, synset2, method='all'):
synset_patt = re.compile(r'^.+\..+\.\d+$')
if synset_patt.match(synset1):
s1 = wn.synset(synset1)
else:
s1 = wn_synset(synset1)
if synset_patt.match(synset2):
s2 = wn.synset(synset2)
else:
s2 = wn_synset(synset2)
if s1 is None or s2 is None:
return 0
if method == 'lin':
return wn.lin_similarity(s1, s2, wn_ic)
elif method == 'res':
return wn.res_similarity(s1, s2, wn_ic)
elif method == 'jcn':
return wn.jcn_similarity(s1, s2, wn_ic)
elif method == 'wup':
return wn.wup_similarity(s1, s2)
elif method == 'path':
return wn.path_similarity(s1, s2)
elif method == 'lch':
return wn.lch_similarity(s1, s2)
elif method == 'all':
return [
('lin', wn.lin_similarity(s1, s2, wn_ic)),
('res', wn.res_similarity(s1, s2, wn_ic)),
('jcn', wn.jcn_similarity(s1, s2, wn_ic)),
('wup', wn.wup_similarity(s1, s2)),
('path', wn.path_similarity(s1, s2)),
('lch', wn.lch_similarity(s1, s2))
]
def create_graphs(doc_list):
documents = doc_list
if documents is None:
documents = default_document_list()
distance_functions = [
(wn.lch_similarity(SYNSETS[0], SYNSETS[0]), 'lch',
lambda sense_1, sense_2: wn.lch_similarity(sense_1, sense_2)),
(1.0, 'lin',
lambda sense_1, sense_2: wn.lin_similarity(sense_1, sense_2, CORPUS)),
(10.636958516573292, 'res',
lambda sense_1, sense_2: wn.res_similarity(sense_1, sense_2, CORPUS)),
(wn.jcn_similarity(SYNSETS[0], SYNSETS[0], CORPUS), 'jcn',
lambda sense_1, sense_2: wn.jcn_similarity(sense_1, sense_2, CORPUS)),
(1.0, 'path',
lambda sense_1, sense_2: wn.path_similarity(sense_1, sense_2)),
]
all_senses = []
for doc in documents:
for sense in doc.top_senses():
all_senses.append((sense, doc.name))
against_colors = ['r', 'b', 'g']
against_to = [
wn.synset(word)
for word in ["economy.n.01", "philosophy.n.02", "politics.n.01"]
]
create_against_graph('phyl_eco_pol', documents, all_senses, against_to,
distance_functions, against_colors)
against_to = SYNSETS
against_colors = [(random(), random(), random())
for _i in range(0, len(SYNSETS))]
create_against_graph('handpicked', documents, all_senses, against_to,
distance_functions, against_colors)
create_graph_top_senses(documents, all_senses, distance_functions)
def __word_net_lch_eval(self, hint: str, target: str):
h_synsets = wn.synsets(hint)
t_synsets = wn.synsets(target)
lst = []
for h in h_synsets:
for t in t_synsets:
try:
strength = wn.lch_similarity(h, t)
except WordNetError:
strength = -1
lst.append(strength if strength is not None else -1)
if all([x == -1 for x in lst]):
return -9.999
else:
return max(lst) # get strongest hint
def wn_similarity(synset_1, synset_2, similarity='Shortest_Path'):
if similarity == "Shortest_Path":
sim = wn.path_similarity(synset_1, synset_2)
elif similarity == "Leacock_Chodorow":
sim = wn.lch_similarity(synset_1, synset_2)
elif similarity == "Wu_Palmer":
sim = wn.wup_similarity(synset_1, synset_2)
elif similarity == "Resnik":
sim = synset_1.res_similarity(synset_2, ic)
elif similarity == "Jiang_Conrath":
sim = synset_1.jcn_similarity(synset_2, ic)
elif similarity == "Lin":
sim = synset_1.lin_similarity(synset_2, ic)
else:
sim = 0
return sim
def assignToCategoriesLCH(category_synsets,word_synsets):
prettyprint("start assigning lch_similarity...")
assignedDict = {}
for category in category_synsets:
assignedDict[category] = []
for word in word_synsets:
tempValues = []
for category in category_synsets:
#similarity = wn.path_similarity(word, category)
similarity = wn.lch_similarity(word, category)
tempValues.append(similarity)
#print("appended "+str(similarity)+" for "+str(category)+ " and "+str(word))
#print("__________________________________")
indexOfMaxValue = tempValues.index(getMaxFromList(tempValues))
assignedDict[ category_synsets[ indexOfMaxValue ] ].append( word )
return assignedDict
def lch_sim(word1,word2):
"""
Leacock-Chodorow Similarity: Return a score denoting how similar
two word senses are, based on the shortest path that connects
the senses (as above) and the maximum depth of the taxonomy in
which the senses occur. range 3.6
The relationship is given as -log(p/2d) where p is the
shortest path length and d the taxonomy depth.
"""
try:
try:
value = wn.lch_similarity(word1, word2)
value = value / 3.6 #value in range of 0 to 1
return value
except ValueError:
return 0
except:
return 0
def similarity(words: list)->list:
'''Calculates similarity based on the given synsets'''
results = []
synsets = ask_for_word_defs(words)
print("\n{}\n".format('*'*80))
for i in range(int(len(synsets)/2)):
print("{:30}{}".format(str(synsets[2*i]), str(synsets[2*i + 1])))
print("\n{}\n".format('*'*80))
print("Running comparisons...")
for i in range(int(len(synsets)/2)):
try:
if (synsets[2*i] == None or synsets[2*i + 1] == None):
results.append(["Undefined","Undefined", -1, -1, -1, "None", "None"])
continue
except:
pass
result = [words[2*i], words[2*i + 1], 0, 0, 0, synsets[2*i].definition(), synsets[2*i + 1].definition()]
result[2] = wordnet.lch_similarity(synsets[2*i],synsets[2*i + 1])
result[3] = wordnet.wup_similarity(synsets[2*i],synsets[2*i + 1])
result[4] = wordnet.path_similarity(synsets[2*i],synsets[2*i + 1])
results.append(result)
print("\n{}\n".format('*'*80))
return results
def word_similarity(self, w1, w2, syns, loc, thr_sim):
syn1 = wn.synsets(w1, wn.NOUN or wn.ADJ)
syn2 = wn.synsets(w2, wn.NOUN or wn.ADJ)
if len(syn1) > 0 and len(syn2) > 0:
score = 0
max_score = 0
count = 0
sns1 = syn1[0]
sns2 = syn2[0]
for i in range(0, len(syn1)):
for j in range(0, len(syn2)):
if self.wordnet_metric == 'j': # Jiang-Conrath Similarity
score = wn.jcn_similarity(syn1[i], syn2[j])
elif self.wordnet_metric == 'le': # Leacock-Chodorow Similarity
score = wn.lch_similarity(syn1[i],
syn2[j],
simulate_root=False)
elif self.wordnet_metric == 'li': # Lin Similarity
score = wn.lin_similarity(syn1[i], syn2[j])
elif self.wordnet_metric == 'p': # Path Similarity
score = wn.path_similarity(syn1[i], syn2[j])
elif self.wordnet_metric == 'w': # Wu-Palmer Similarity. It can not be '0'. It ranges in (0,1]
score = wn.wup_similarity(syn1[i], syn2[j])
if score > max_score: # Finding the maximum score
max_score = score
sns1 = syn1[i]
sns2 = syn2[j]
if max_score >= thr_sim: # Storing all the synset pairs that have scores > threshold
syns, loc = self.merging_synsets(
syns, w1, w2, sns1, sns2, max_score, loc)
count = count + 1
if count == 0: # Storing the synset that has maximum score but the score < threshold
syns, loc = self.merging_synsets(syns, w1, w2, sns1, sns2,
max_score, loc)
return syns, loc
def word_similarity(measure, word1, word2, pos):
wsim = 0.0
if pos is "n" or pos is "v":
if pos is "n":
word1 = wn.synsets(word1, wn.NOUN)
word2 = wn.synsets(word2, wn.NOUN)
else:
word1 = wn.synsets(word1, wn.VERB)
word2 = wn.synsets(word2, wn.VERB)
if word1 != [] and word2 != []:
word1 = word1[0]
word2 = word2[0]
if measure == "path":
wsim = wn.path_similarity(word1, word2)
if measure == "lch":
wsim = wn.lch_similarity(word1, word2)
wsim = (wsim / 3.63758615973)
if measure == "wup":
wsim = wn.wup_similarity(word1, word2)
if measure == "res":
wsim = word1.res_similarity(word2, brown_ic)
wsim = wsim / 9.00601439892
if measure == "jcn":
wsim = word1.jcn_similarity(word2, brown_ic)
wsim = wsim / (1e+300)
if measure == "lin":
wsim = word1.lin_similarity(word2, brown_ic)
return wsim
else:
return wsim
else:
if pos is "r" or pos is "a":
if word1 is word2:
return 1.0
else:
return 0.0
def lch_similarity(synsets1, synsets2):
"""
This function returns Leacock Chodorow similarity (LCH)
between two synsets, based on the shortest path distance
and the maximum depth of the taxonomy. The equation to
calculate LCH similarity is shown below:
.. math::
lch\\_similarity = {-log(shortest\\_path\\_distance(synsets1,
synsets2) \\over 2 * taxonomy\\_depth}
:param `Synset` synsets1: first synset supplied to measures
the LCH similarity
:param `Synset` synsets2: second synset supplied to measures
the LCH similarity
:return: LCH similarity between two synsets
:rtype: float
:Example:
>>> from pythainlp.corpus.wordnet import lch_similarity, synset
>>>
>>> entity = synset('entity.n.01')
>>> obj = synset('object.n.01')
>>> cat = synset('cat.n.01')
>>>
>>> lch_similarity(entity, obj)
2.538973871058276
>>> lch_similarity(entity, cat)
0.9985288301111273
>>> lch_similarity(obj, cat)
1.1526795099383855
"""
return wordnet.lch_similarity(synsets1, synsets2)
def similarity_by_path(sense1, sense2, option="path", no_path_value=0):
"""
Returns maximum path similarity between two senses.
If no path is found between the two senses, returns no_path_value.
"""
if option.lower() in ["path", "path_similarity"]: # Path similaritys
sim_dir1 = wn.path_similarity(sense1, sense2)
sim_dir2 = wn.path_similarity(sense2, sense1)
if sim_dir1 is None and sim_dir2 is None:
return no_path_value
elif sim_dir1 is None:
return sim_dir2
elif sim_dir2 is None:
return sim_dir1
else:
return max(sim_dir2, sim_dir1)
elif option.lower() in ["wup", "wupa", "wu-palmer",
"wu-palmer"]: # Wu-Palmer
wup_sim = wn.wup_similarity(sense1, sense2)
return wup_sim if wup_sim is not None else no_path_value
elif option.lower() in ['lch', "leacock-chordorow"]: # Leacock-Chodorow
if sense1.pos != sense2.pos: # lch can't do diff POS
return no_path_value
return wn.lch_similarity(sense1, sense2)
distances_res_bnc.append(sim7)
distances_jcn_bnc.append(sim8)
distances_lin_bnc.append(sim9)
#Import IC calculation
from nltk.corpus import wordnet_ic
brown_ic = wordnet_ic.ic('ic-brown-resnik-add1.dat')
bnc_ic = wordnet_ic.ic('ic-bnc-resnik-add1.dat')
#For each pair of synsets, compute distance
for s1 in synsets:
syn1 = wn.of2ss(s1)
for s2 in synsets:
syn2 = wn.of2ss(s2)
distances_path[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1 - wn.path_similarity(syn1,syn2)
distances_lch[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1 - wn.lch_similarity(syn1,syn2)
distances_wup[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1 - wn.wup_similarity(syn1,syn2)
distances_res[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1 - wn.res_similarity(syn1,syn2,brown_ic)
distances_jcn[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1 - wn.jcn_similarity(syn1,syn2,brown_ic)
distances_lin[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1 - wn.lin_similarity(syn1,syn2,brown_ic)
distances_res_bnc[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1 - wn.res_similarity(syn1,syn2,bnc_ic)
distances_jcn_bnc[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1 - wn.jcn_similarity(syn1,syn2,bnc_ic)
distances_lin_bnc[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1 - wn.lin_similarity(syn1,syn2,bnc_ic)
#distances_path[labelsNLTK.index(s1)][labelsNLTK.index(s2)] =1/(labelsNLTK.index(s2)+1)
#distances_lch[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1/(labelsNLTK.index(s2)+1)
#distances_wup[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1/(labelsNLTK.index(s2)+1)
#distances_res[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1/(labelsNLTK.index(s2)+1)
#distances_jcn[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1/(labelsNLTK.index(s2)+1)
#distances_lin[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1/(labelsNLTK.index(s2)+1)
#distances_res_bnc[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1/(labelsNLTK.index(s2)+1)
#distances_jcn_bnc[labelsNLTK.index(s1)][labelsNLTK.index(s2)] = 1/(labelsNLTK.index(s2)+1)
def lch_similarity(synset1, synset2):
return wn.lch_similarity(synset1, synset2)
#coeffs['drink'] = 1.0-float(sys.argv[4])
coeffs['eat'] = 1.0
coeffs['drink'] = 1.0
for s in sentences:
s = s.split()
if s[1] not in verbs_gref:
verbs_gref[s[1]] = np.zeros((num_basis, num_basis))
verbs_gref[s[1]] = verbs_gref[s[1]]+np.outer(matrix[s[0]],matrix[s[2]])
verbs[s[1]] = np.zeros((num_basis, num_basis))
simweights = {}
for v in verbs:
if sys.argv[3]=='wup':
simweights[v] = wn.wup_similarity(wn.synset(s[1]+'.v.01'), wn.synset(v+'.v.01'))
elif sys.argv[3]=='lch':
simweights[v] = wn.lch_similarity(wn.synset(s[1]+'.v.01'), wn.synset(v+'.v.01'))
elif sys.argv[3]=='path':
simweights[v] = wn.path_similarity(wn.synset(s[1]+'.v.01'), wn.synset(v+'.v.01'))
verbs[s[1]] += coeffs[v]*simweights[v]*verbs_gref[v]
verbs[s[1]] /= float(sum(simweights.values()))
# Learn the reference matrices using Grefenstette for swallow consume and gulp
gold_verbs = ['swallow','consume','gulp']
for gv in gold_verbs:
with open('train/'+gv+'_train') as f:
sentences = f.readlines()
for s in sentences:
s = s.split()
if s[1] not in verbs_gref:
verbs_gref[s[1]] = np.zeros((num_basis, num_basis))
verbs_gref[s[1]] = verbs_gref[s[1]]+np.outer(matrix[s[0]],matrix[s[2]])
def wn_similarity(synset_1, synset_2):
return wn.lch_similarity(synset_1, synset_2)
def lch_similarity(synsets1,synsets2): return wordnet.lch_similarity(synsets1,synsets2)
def lch_sim_fun(vq_words=[]):
l1 = knowledge = [
'recite', 'review', 'point', 'recognize', 'describe', 'choose',
'examine', 'identify', 'enumerate', 'find', 'select', 'what',
'memorize', 'collect', 'sequence', 'when', 'duplicate', 'who', 'label',
'write', 'indicate', 'state', 'tabulate', 'which', 'relate', 'show',
'arrange', 'cite', 'match', 'define', 'locate', 'draw', 'repeat',
'remember', 'trace', 'read', 'quote', 'spell', 'memorise', 'how',
'observe', 'recognise', 'copy', 'why', 'outline', 'count', 'name',
'recall', 'study', 'omit', 'list', 'tell', 'reproduce', 'record',
'retell', 'meet', 'listen', 'where', 'order', 'view'
]
l2 = comprehension = [
'compare', 'cite', 'give', 'predict', 'recognize', 'describe',
'articulate', 'detail', 'order', 'characterize', 'generalize',
'factor', 'summarize', 'select', 'illustrate', 'visualize', 'group',
'trace', 'purpose', 'defend', 'rewrite', 'relate', 'approximate',
'demonstrate', 'indicate', 'add', 'interact', 'tell', 'extrapolate',
'show', 'rephrase', 'paraphrase', 'infer', 'contrast', 'locate',
'picture', 'extend', 'associate', 'conclude', 'express', 'interpolate',
'generalise', 'clarify', 'observe', 'understand', 'differentiate',
'review', 'distinguish', 'estimate', 'subtract', 'discuss',
'interpret', 'summarise', 'convert', 'translate', 'compute', 'outline',
'identify', 'elaborate', 'ask', 'example', 'classify', 'report',
'restate', 'explain', 'match'
]
l3 = application = [
'represent', 'show', 'identify', 'participate', 'derive', 'group',
'calculate', 'graph', 'dramatize', 'choose', 'factor', 'include',
'allocate', 'handle', 'practice', 'relate'
'schedule', 'report', 'assess', 'collect', 'investigate', 'categorise',
'ascertain', 'round', 'sketch', 'transcribe', 'sequence', 'imitate',
'discover', 'connect', 'tabulate', 'employ', 'avoid', 'experiment',
'manipulate', 'exercise', 'extend', 'associate', 'modify',
'personalize', 'dramatise', 'explore', 'teach', 'change', 'perform',
'summarise', 'act', 'implement', 'assign', 'alphabetize', 'relate',
'articulate', 'administer', 'subscribe', 'instruct', 'determine',
'apply', 'establish', 'select', 'illustrate', 'plot', 'use', 'prepare',
'paint', 'transfer', 'construct', 'process', 'interpret', 'translate',
'depreciate', 'complete', 'expose', 'acquire', 'adapt', 'link',
'simulate', 'diminish', 'compute', 'project', 'demonstrate', 'control',
'predict', 'contribute', 'examine', 'attain', 'capture', 'develop',
'provide', 'utilize', 'write', 'build', 'interview', 'organise',
'classify', 'draw', 'express', 'customize', 'price', 'chart',
'produce', 'plan', 'inform', 'solve', 'correlation', 'model',
'operate', 'convert'
]
l4 = analysis = [
'find', 'focus', 'identify', 'query', 'debate', 'relationships',
'derive', 'group', 'calculate', 'explain', 'theme', 'choose', 'reason',
'proof', 'reorganise', 'point', 'interrupt', 'difference', 'arrange',
'list', 'investigate', 'classify', 'discover', 'motive', 'deduce',
'connect', 'advertise', 'detect', 'confirm', 'research', 'experiment',
'size', 'cause', 'contrast', 'inspect', 'explore', 'distinguish',
'layout', 'optimize', 'interpret', 'question', 'omit', 'depth',
'ensure', 'distinction', 'inference', 'divide', 'relate', 'manage',
'rank', 'maximize', 'categorize', 'establish', 'select', 'illustrate',
'subdivide', 'transform', 'comparing', 'assumption', 'analyze',
'function', 'analyse', 'train', 'differentiate', 'breadboard',
'dissect', 'see', 'limit', 'highlight', 'appraise', 'diagnose',
'blueprint', 'compare', 'recognize', 'characterize', 'examine', 'file',
'discriminate', 'discussion', 'isolate', 'inventory', 'test', 'survey',
'document', 'infer', 'categorise', 'breakdown', 'separate', 'effect',
'diagram', 'simplify', 'point', 'audit', 'criticize', 'outline',
'correlate', 'minimize', 'prioritize', 'organise', 'model', 'order',
'test'
]
l5 = synthesis = [
'incorporate', 'code', 'reorganize', 'invent', 'generalize', 'compose',
'overhaul', 'explain', 'hypothesize', 'program', 'combine', 'choose',
'frame', 'integrate', 'collaborate', 'handle', 'format', 'propose',
'express', 'progress', 'reconstruct', 'speculate', 'discuss', 'comply',
'arrange', 'intervene', 'collect', 'hypothesise', 'debug', 'enhance',
'anticipate', 'originate', 'formulate', 'discover', 'reinforce',
'design', 'animate', 'substitute', 'network', 'join', 'experiment',
'adapt', 'lecture', 'contrast', 'extend', 'visualise', 'modify',
'makeup', 'prescribe', 'imagine', 'interface', 'estimate', 'generate',
'change', 'improve', 'convert', 'elaborate', 'initiate',
'individualize', 'think', 'revise', 'organize', 'relate', 'assemble',
'synthesize', 'categorize', 'summarize', 'prepare', 'create',
'transform', 'construct', 'predict', 'theorise', 'minimise', 'tell',
'cope', 'maximise', 'innovate', 'specify', 'communicate', 'setup',
'pretend', 'budget', 'compile', 'suppose', 'tabulate', 'delete',
'compare', 'rewrite', 'devise', 'abstract', 'dictate', 'cultivate',
'happen', 'portray', 'depict', 'develop', 'perform', 'make', 'write',
'build', 'test', 'negotiate', 'rearrange', 'simplify', 'produce',
'plan', 'validate', 'structure', 'add', 'outline', 'facilitate',
'correspond', 'solve', 'model', 'original'
]
l6 = evaluation = [
'validate', 'compare', 'deduct', 'useful', 'consider', 'conclude',
'predict', 'relate', 'describe', 'influence', 'rank', 'assess', 'rate',
'persuade', 'determine', 'measure', 'critique', 'mark', 'summarize',
'select', 'discuss', 'discriminate', 'prove', 'verify', 'defend',
'support', 'debate', 'grade', 'argue', 'disprove', 'recommend', 'test',
'infer', 'contrast', 'choose', 'attach', 'good', 'importance',
'evaluate', 'criteria', 'prescribe', 'hire', 'award', 'perceive',
'dispute', 'know', 'decide', 'opinion', 'judge', 'estimate', 'why',
'interpret', 'counsel', 'criticize', 'effective', 'prioritize',
'value', 'agree', 'bad', 'convince', 'prioritise', 'release', 'frame',
'appraise', 'explain', 'criticise', 'justify'
]
cl_listoflist = []
cl_listoflist.append(l1)
cl_listoflist.append(l2)
cl_listoflist.append(l3)
cl_listoflist.append(l4)
cl_listoflist.append(l5)
cl_listoflist.append(l6)
cnt_log = 0
final_level_of_ques = -1
final_sim_of_ques_with_all_levels = [0, 0, 0, 0, 0, 0]
final_area_sim_of_ques_with_all_levels = [0, 0, 0, 0, 0, 0]
for vq_word in vq_words:
# calculating sum and avg of sim of word with each list
# print("\n\ndoing for word -----" , vq_word)
sum_of_sim_all_levels = []
avg_of_sim_all_levels = []
for i, list_i in enumerate(cl_listoflist):
# print("list number : " , i)
sum_of_sim = 0
for l_word in list_i:
# print("two words " , vq_word , l_word)
if len(wordnet.synsets(vq_word)) == 0:
# print vq_word
break
vq_word_syn = wordnet.synsets(vq_word)[0]
# print("l_word => wordnet.synsets(l_word)",l_word, "=>" ,wordnet.synsets(l_word))
if len(wordnet.synsets(l_word)) == 0:
# print l_word
continue
l_word_syn = wordnet.synsets(l_word)[0]
try:
wup_sim = wordnet.lch_similarity(vq_word_syn, l_word_syn)
except:
# print vq_word_syn,l_word_syn,"->exception"
continue
# wup_sim=(vq_word_syn).jcn_similarity(l_word_syn)
if (type(wup_sim) != type(None)):
sum_of_sim = sum_of_sim + wup_sim
# sum_of_sim += 1
# print(" counted ",vq_word,l_word , "synset " , vq_word_syn , l_word_syn)
else:
cnt_log = cnt_log + 1
# print("Not counted ",vq_word,l_word , "synset " , vq_word_syn , l_word_syn)
# input()
sum_of_sim_all_levels.append(sum_of_sim)
avg_of_sim_all_levels.append(sum_of_sim / len(list_i))
# print("\n\n printing all lists")
# for l in cl_listoflist:
# print(l)
# QUES WORK BEGIN
# print ("Sim")
for i in range(0, 6):
final_sim_of_ques_with_all_levels[i] += avg_of_sim_all_levels[i]
# print (final_sim_of_ques_with_all_levels[i],",")
# print("\n")
# print("area sim")
for i in range(0, 6):
final_area_sim_of_ques_with_all_levels[i] += sum_of_sim_all_levels[
i]
# print (final_area_sim_of_ques_with_all_levels[i],",")
# print("\n")
# print ("cnt_log",cnt_log)
# print ("Final Sim")
# for i in range(0,6):
# print (final_sim_of_ques_with_all_levels[i],",")
# print("\n")
# print ("Final Area Sim")
# for i in range(0,6):
# print (final_area_sim_of_ques_with_all_levels[i],",")
# print("\n")
# maximum of all similarities values to find cl level
final_level = 0
max_sim = final_sim_of_ques_with_all_levels[0]
for index, sim in enumerate(final_sim_of_ques_with_all_levels):
if sim > max_sim:
max_sim = sim
final_level = index
# print("\n")
# print("avg wali list: " , avg_of_sim_all_levels)
# print( "sum wali list: " , sum_of_sim_all_levels)
# finding if word will be classified in more than two levels
count = 0
indices_of_same_sim = []
for i, sim in enumerate(final_sim_of_ques_with_all_levels):
if sim == max_sim:
count += 1
indices_of_same_sim.append(i)
# if word is in more than two levels
if len(indices_of_same_sim) > 1:
# print ("ques is in more than two levels")
same_sim_list = []
for index in indices_of_same_sim:
same_sim_list.append(final_area_sim_of_ques_with_all_levels[index])
max_sim_area = same_sim_list[0]
for sim_area, index_of_max_sim in zip(same_sim_list,
indices_of_same_sim):
if sim_area > max_sim_area:
max_sim_area = sim_area
final_level = index_of_max_sim
# print("final_level ",final_level)
return final_level
