def get_score(text):
words = get_words(text)
exists = False
max_dist = 0
with open('tools/distances.txt', 'rb') as src:
for word in words:
nodes = []
for form in morph.parse(word):
normal_form = form.normal_form
if get_pos_tag(normal_form) != 'ADJF':
continue
if normal_form not in senses:
continue
for _, id in senses[normal_form].items():
node = int(id[1:])
if node not in nodes:
nodes.append(node)
for i in range(len(nodes)):
for j in range(i + 1, len(nodes)):
dist = get_dist(src, nodes[i], nodes[j])
if not exists or dist > max_dist:
exists = True
max_dist = dist
return max_dist
def get_score(text):
words = get_words(text)
sum_senses_left = 0
for word in words[:len(words) // 2]:
max_senses = 1
for form in morph.parse(word):
normal_form = form.normal_form
if get_pos_tag(normal_form) == 'ADJF' and normal_form in senses:
max_senses = max(max_senses, len(senses[normal_form]))
sum_senses_left += log(max_senses)
sum_senses_right = 0
for word in words[len(words) // 2:]:
max_senses = 1
for form in morph.parse(word):
normal_form = form.normal_form
if get_pos_tag(normal_form) == 'ADJF' and normal_form in senses:
max_senses = max(max_senses, len(senses[normal_form]))
sum_senses_right += log(max_senses)
return [
sum_senses_left + sum_senses_right, sum_senses_left, sum_senses_right
]
def get_score(text):
words = get_words(text)
min_dist = 1
for word in words:
all_senses = []
for form in morph.parse(word):
normal_form = form.normal_form
if get_pos_tag(normal_form) in good_tags:
continue
if normal_form not in senses:
continue
for sense, _ in senses[normal_form].items():
if sense not in all_senses:
all_senses.append(sense)
if len(all_senses) == 0:
continue
bag_of_words = set()
for i in range(len(all_senses)):
for j in range(len(all_senses[i])):
if not all_senses[i][j].isalnum():
all_senses[i] = all_senses[i].replace(all_senses[i][j], ' ')
all_senses[i] = all_senses[i].lower().split()
want = min(len(all_senses[i]), 3)
for sense_word in all_senses[i][:want]:
normal_form = max(morph.parse(sense_word), key=lambda x: x.score).normal_form
normalized_form, _ = get_pos_form(normal_form)
if normalized_form is not None and normalized_form not in bag_of_words and normalized_form in wv.vocab:
bag_of_words.add(normalized_form)
for w1 in bag_of_words:
for w2 in bag_of_words:
if w1 == w2:
continue
min_dist = min(min_dist, wv.similarity(w1, w2))
return min_dist