-
Notifications
You must be signed in to change notification settings - Fork 0
/
get_synonyms.py
59 lines (47 loc) · 1.43 KB
/
get_synonyms.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
import re
import numpy as np
from nltk.corpus import wordnet as wn
# nltk.download('averaged_perceptron_tagger')
# nltk.download('wordnet')
# nltk.download('punkt')
from model_init import model
def get_words(in_word):
syns = wn.synsets(in_word)
ret = []
for syn in syns:
ret_list = []
for lem in syn.lemmas():
lemma_name = lem.name()
lemma_name = lemma_name.replace('_', ' ')
if lemma_name != in_word:
ret_list.append(lemma_name)
if len(ret_list) > 0:
ret.append(ret_list)
return ret
def cosine(u, v):
return np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v))
def get_best_words(word, sent, syn):
parts = re.split(f'{word}', sent)
old_sent = model.encode([sent])[0]
syn_max = -float('inf')
best_words = []
word_max = -float('inf')
best_word = ''
for synset in syn:
total = 0
for w in synset:
new_sent = f'{w}'.join(parts)
sim = cosine(model.encode([new_sent])[0], old_sent)
if sim > word_max:
best_word = w
word_max = sim
total += sim
print(f'{w} and {sim}')
avg = total / len(synset)
print(f'{synset} and {avg}')
if avg > syn_max:
best_words = synset
syn_max = avg
print(best_words)
print(f'best word indiv: {best_word}')
return best_words