def wn_similarity(synset_1, synset_2):
if synset_1.pos() not in [
"a", "s", "r"
] and synset_2.pos() not in ["a", "s", "r"]:
return wn.res_similarity(synset_1, synset_2, brown_ic)
else:
return None
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 similarity_by_infocontent(sense1, sense2, option):
""" Returns similarity scores by information content. """
if sense1.pos != sense2.pos: # infocontent sim can't do diff POS.
return 0
info_contents = [
'ic-bnc-add1.dat', 'ic-bnc-resnik-add1.dat', 'ic-bnc-resnik.dat',
'ic-bnc.dat', 'ic-brown-add1.dat', 'ic-brown-resnik-add1.dat',
'ic-brown-resnik.dat', 'ic-brown.dat', 'ic-semcor-add1.dat',
'ic-semcor.dat', 'ic-semcorraw-add1.dat',
'ic-semcorraw-resnik-add1.dat', 'ic-semcorraw-resnik.dat',
'ic-semcorraw.dat', 'ic-shaks-add1.dat', 'ic-shaks-resnik.dat',
'ic-shaks-resnink-add1.dat', 'ic-shaks.dat', 'ic-treebank-add1.dat',
'ic-treebank-resnik-add1.dat', 'ic-treebank-resnik.dat',
'ic-treebank.dat'
]
if option in ['res', 'resnik']:
return wn.res_similarity(sense1, sense2,
wnic.ic('ic-bnc-resnik-add1.dat'))
#return min(wn.res_similarity(sense1, sense2, wnic.ic(ic)) \
# for ic in info_contents)
elif option in ['jcn', "jiang-conrath"]:
return wn.jcn_similarity(sense1, sense2, wnic.ic('ic-bnc-add1.dat'))
elif option in ['lin']:
return wn.lin_similarity(sense1, sense2, wnic.ic('ic-bnc-add1.dat'))
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 semantic_similarity(word1, word2, speech, measure):
"""
Finds the highest similarity score for the given pair of words. Goes through each combination of all senses.
:param word1: First word in the pair of words
:param word2: Second word in the pair of words
:param speech: part of speech e.g. nw.NOUN
:param measure: String representing the type of similarity measure ("path" = path ; "res" = Resnik ; "lin" = Lin)
:return: The highest similarity score across all senses and all parts of speech
"""
#error handling if invalid measure input is given
if measure not in ["path","res","lin"]:
raise ValueError("Not a valid similarity type \n Must be 'path'(path), 'res'(Resnik) or 'lin'(Lin)")
greatest = 0
conceptsA = wn.synsets(word1,speech)
conceptsB = wn.synsets(word2,speech)
#finds similarity score for every combination of senses
for conceptA in conceptsA:
for conceptB in conceptsB:
if measure == "path":
similarity = wn.path_similarity(conceptA,conceptB)
elif measure == "res":
similarity = wn.res_similarity(conceptA,conceptB,brown_ic)
elif measure == "lin":
similarity = wn.lin_similarity(conceptA,conceptB,brown_ic)
if similarity == None : continue #error checking if similarity scorce not possible
if similarity>greatest:
greatest = similarity #if new highest similairty is found, set it to the greatest
return greatest
def get_res_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
# Try find similarity from corpus
try:
similarity = wordnet.res_similarity(
synsets_word1[0], synsets_word2[0], self.brown_ic)
except:
continue
if similarity != None:
min_similarity = min(similarity, min_similarity)
if min_similarity == maxint:
return 0
return min_similarity
def res(self, synset_a, synset_b, ic):
return (
self.normalize(
self.MAX_VALUE,
wordnet.res_similarity(synset_a, synset_b, ic),
)
if synset_a.pos() == synset_b.pos()
else 0
)
def test_wordnet_ic(self):
from nltk.corpus import wordnet as nltk_wn
from nltk.corpus import wordnet_ic as nltk_wnic
nltk_car = nltk_wn.synset('car.n.1')
nltk_bus = nltk_wn.synset('bus.n.1')
our_bnc_resnik_add1 = WordNetInformationContent('bnc', resnik=True, add1=True)
our_car = our_wn.synset('car.n.1')
our_bus = our_wn.synset('bus.n.1')
nltk_bnc_resnik_add1 = nltk_wnic.ic('ic-bnc-resnik-add1.dat')
assert our_wn.res_similarity(our_car, our_bus, our_bnc_resnik_add1) == nltk_wn.res_similarity(nltk_car, nltk_bus, nltk_bnc_resnik_add1)
assert our_wn.jcn_similarity(our_car, our_bus, our_bnc_resnik_add1) == nltk_wn.jcn_similarity(nltk_car, nltk_bus, nltk_bnc_resnik_add1)
assert our_wn.lin_similarity(our_car, our_bus, our_bnc_resnik_add1) == nltk_wn.lin_similarity(nltk_car, nltk_bus, nltk_bnc_resnik_add1)
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 get_res_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
# Try find similarity from corpus
try:
similarity = wordnet.res_similarity(
synsets_word1[0], synsets_word2[0], self.brown_ic)
except:
continue
if similarity == 1e+300:
similarity = 12.0
if similarity != None:
avg_similarity += similarity
total_count += 1
if total_count == 0:
return 0
return float(avg_similarity) / float(total_count)
def get_sim_score(word_1, word_2, info_content):
"""
Calculate the highest path similarity among all pairs.
"""
if word_1 == word_2:
return 1
else:
max_sim = -1.0
synsets_1 = wn.synsets(word_1)
synsets_2 = wn.synsets(word_2)
if synsets_1 and synsets_2:
for synset_1, synset_2 in product(synsets_1, synsets_2):
try:
sim = wn.res_similarity(synset_1, synset_2, info_content)
#sim = wn.wup_similarity(synset_1, synset_2)
if sim > max_sim:
max_sim = sim
except:
continue
return max_sim
return max_sim
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 res_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.res_similarity(vq_word_syn, l_word_syn,
brown_ic)
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
#simFunction = wn.path_similarity # Leacock-Chodorow Similarity: -log(p/2d) where p is the shortest path length and d the taxonomy depth. # 不同词性的直接认为值为0 #0.303463230095 0.33365080482 0.243127578189 #simFunction = lambda x,y : wn.lch_similarity(x, y) if x.pos()==y.pos() else 0 # based on the depth of the two senses in the taxonomy and that of their Least Common Subsumer #0.331197865969 0.35898821251 0.258938723468 #simFunction = wn.wup_similarity # -log P(LCS(c1,c2)) #0.332605157422 0.378351460201 0.237095177379 #simFunction = lambda x,y : wn.res_similarity(x,y,brown_ic)if x.pos()==y.pos() and not x.pos()in['a','s'] else 0 #0.333519003413 0.382270850924 0.237229430736 #simFunction = lambda x,y : wn.res_similarity(x,y,semcor_ic)if x.pos()==y.pos() and not x.pos()in['a','s'] else 0 #0.327047195075 0.378426849209 0.226561164029 simFunction = lambda x, y: wn.res_similarity(x, y, genesis_ic) if x.pos( ) == y.pos() and not x.pos() in ['a', 's'] else 0 # 1/(IC(s1) + IC(s2) - 2 * IC(lcs)) #0.283453351637 0.364845938375 0.158729843246 #simFunction = lambda x,y : wn.jcn_similarity(x,y,brown_ic)if x.pos()==y.pos() and not x.pos()in['a','s'] else 0 #0.17733734237 0.272078927265 0.0570884272107 #simFunction = lambda x,y : wn.jcn_similarity(x,y,semcor_ic)if x.pos()==y.pos() and not x.pos()in['a','s'] else 0 #0.131891595045 0.215981129294 0.0797506187655 #simFunction = lambda x,y : wn.jcn_similarity(x,y,genesis_ic)if x.pos()==y.pos() and not x.pos()in['a','s'] else 0 # 2 * IC(lcs) / (IC(s1) + IC(s2)) #0.299336498295 0.360000100519 0.197707567689 #simFunction = lambda x,y : wn.lin_similarity(x,y,brown_ic)if x.pos()==y.pos() and not x.pos()in['a','s'] else 0 #0.216159121464 0.273284313725 0.12954361359 #simFunction = lambda x,y : wn.lin_similarity(x,y,semcor_ic)if x.pos()==y.pos() and not x.pos()in['a','s'] else 0
def res_similarity(synsets1, synsets2):
similarity_function = lambda ss1, ss2: wn.res_similarity(ss1, ss2, corpus)
return __max_similarity(synsets1, synsets2, similarity_function)
def resnik_similarity(a,b): if wn.res_similarity(wn.synsets(a)[0],wn.synsets(b)[0],genesis_ic) == None: return 0 else: return wn.res_similarity(wn.synsets(a)[0],wn.synsets(b)[0],genesis_ic)
