for s1 in senses[w1]:
for s2 in senses[w2]:
if (sense_vectors.has_key(s1) and sense_vectors.has_key(s2)):
m_score = m_score + cos_function(sense_vectors[s1] , sense_vectors[s2])
if ( len(senses[w1]) > 0.0 and len(senses[w2]) > 0.0):
m_score = m_score / ( float( len(senses[w1] ) ) * float ( len(senses[w2])) )
else:
continue
if ( m_score > 0.0 ):
machine_score.append(m_score)
human_score.append(float(p[2]))
p_val, p_rel = sci.stats.spearmanr(human_score, machine_score)
print "Simple Linear Approach", p_val
if __name__ == "__main__":
word_vectors = nlp.read_word_vectors(VECTOR_DIR + VECTOR_NAME)
word_pairs = nlp.read_csv(CSV_DIR + CSV_NAME)
vocab = []
for p in word_pairs:
vocab.append(p[0].lower())
vocab.append(p[1].lower())
vocab = list(set(vocab))
for w in vocab:
word_hypernyms[w] = nlp.read_hypernyms(w)
word_hyponyms[w] = nlp.read_hyponyms(w)
word_synonyms[w] = nlp.read_synonyms(w)
senses[w] = nlp.read_senses(w)
for s in senses[w]:
sense_vectors[s] = np.zeros(VECTOR_DIM)
sense_hypernyms[s] = nlp.read_hypernyms_by_sense(s)
sense_hyponyms[s] = nlp.read_hyponyms_by_sense(s)
sense_synonyms[s] = nlp.read_synonyms_by_sense(s)
result = 0.0
for l in s.lemmas():
wd = l.name()
dis = alpha * np.linalg.norm(word_pool[wd] - sense_vectors[s])
dis = np.power(dis, 2)
dis1 = beta * np.linalg.norm(sense_vectors[s] - sense_pool[s])
dis1 = np.power(dis1, 2)
result = result + dis + dis1
result = 0.5 * result
return result
if __name__ == "__main__":
print "VECTOR:", VECTOR_NAME
word_vectors = nlp.read_word_vectors(VECTOR_DIR + VECTOR_NAME)
word_pairs = nlp.read_csv(CSV_DIR + CSV_NAME)
print "CSV", CSV_NAME
vocab = []
for p in word_pairs:
vocab.append(p[0].lower())
vocab.append(p[1].lower())
vocab = list(set(vocab))
for w in vocab:
word_hypernyms[w] = nlp.read_hypernyms(w)
word_hyponyms[w] = nlp.read_hyponyms(w)
word_synonyms[w] = nlp.read_synonyms(w)
senses[w] = nlp.read_senses(w)
for s in senses[w]:
sense_vectors[s] = np.zeros(VECTOR_DIM)
sense_hypernyms[s] = nlp.read_hypernyms_by_sense(s)
sense_hyponyms[s] = nlp.read_hyponyms_by_sense(s)
csv_path = "../../csv/"
vector_path = "../test_vector/"
vector_file = "100_6.vec"
vector_dim = [100]
vector_win = [6]
def cos_function(v1,v2):
result = 0.0
tp = np.dot(v1, np.transpose(v2))
btm = np.linalg.norm(v1) * np.linalg.norm(v2)
result = tp/btm
return result
if __name__ == "__main__":
for dim in vector_dim:
for win in vector_win:
target_vector = str(dim) + "_" + str(win) + ".vec"
word_vectors = nlp.read_word_vectors(vector_path + target_vector)
print "current vec",target_vector
print "current file", target_file
word_pairs = nlp.read_csv(csv_path + target_file)
m_score = []
h_score = []
for p in word_pairs:
if ( word_vectors.has_key(p[0].lower()) and word_vectors.has_key(p[1].lower())):
h_score.append(float(p[2]))
m_score.append(cos_function(word_vectors[p[0].lower()], word_vectors[p[1].lower()]))
else:
print "Word1",p[0], "Word2",p[1],"Ignored"
p_val,p_dif = sci.stats.spearmanr(h_score,m_score)
print "Spearmanr Single", str(p_val)
else:
up_time = 0
# learning_rate = learning_rate - 0.005
if (p_rel >= 80.0 or up_time > 4):
break
print "this time error", sum_error
print "former time error", former_sum_error
print "up_time", up_time
print "learning_rate", learning_rate
print "pearson", sci.stats.pearsonr(human_s, machine_s)
former_sum_error = sum_error
if __name__ == "__main__":
print "read vector & score"
word_vectors = nlplib.read_word_vectors("./test_vector/100_3.vec")
word_pair_score = nlplib.read_csv("./csv/R&G-65.csv")
######Read hypernyms
print "get hypernyms etc."
for w_pair_score in word_pair_score:
word_dictionary.append(w_pair_score[0])
word_dictionary.append(w_pair_score[1])
# remove dumplicated word, for we are searching hypernyms, hyponyms, synonyms according to the dictionary.
word_dictionary = list(set(word_dictionary))
for w in word_dictionary:
word_hypernyms[w] = nlplib.read_hypernyms(w)
word_hyponyms[w] = nlplib.read_hyponyms(w)
word_synonyms[w] = nlplib.read_synonyms(w)
print "start training"
cnn_training()
else:
up_time = 0
# learning_rate = learning_rate + 0.3
if (p_rel >= 80.0 and up_time == 1 ):
break
print "this time error", sum_error
print "former time error", former_sum_error
print "up_time",up_time
print "learning_rate", learning_rate
print "pearson", sci.stats.pearsonr(human_s, machine_s)
print "iteration time", iteration_time
iteration_time = iteration_time + 1
former_sum_error = sum_error
if __name__=="__main__":
print "read vector & score"
word_vectors = nlplib.read_word_vectors("./test_vector/100_3.vec")
word_pair_score = nlplib.read_csv("./csv/R&G-65.csv")
######Read hypernyms
print "get hypernyms etc."
for w_pair_score in word_pair_score:
word_dictionary.append(w_pair_score[0])
word_dictionary.append(w_pair_score[1])
# remove dumplicated word, for we are searching hypernyms, hyponyms, synonyms according to the dictionary.
word_dictionary = list(set(word_dictionary))
for w in word_dictionary:
word_hypernyms[w] = nlplib.read_hypernyms(w)
word_hyponyms[w] = nlplib.read_hyponyms(w)
word_synonyms[w] = nlplib.read_synonyms(w)
print "start training"
cnn_training()