腾讯开源词向量下载地址
导入词向量
from gensim.models import KeyedVectors
file = 'Tencent_AILab_ChineseEmbedding/Tencent_AILab_ChineseEmbedding.txt'
wv_from_text = KeyedVectors.load_word2vec_format(file, binary=False)
wv_from_text.init_sims(replace=True) # 神奇，很省内存，可以运算most_similar
未知词短语向量补齐：
# 未知词、短语向量补齐
import numpy as np
def compute_ngrams(word, min_n, max_n):
#BOW, EOW = ('<', '>') # Used by FastText to attach to all words as prefix and suffix
extended_word = word
ngrams = []
for ngram_length in range(min_n, min(len(extended_word), max_n) + 1):
for i in range(0, len(extended_word) - ngram_length + 1):
ngrams.append(extended_word[i:i + ngram_length])
return list(set(ngrams))
def wordVec(word, wv_from_text, min_n = 1, max_n = 3):
'''
ngrams_single/ngrams_more,主要是为了当出现oov的情况下,最好先不考虑单字词向量
'''
# 确认词向量维度
word_size = wv_from_text.wv.syn0[0].shape[0]
# 计算word的ngrams词组
ngrams = compute_ngrams(word,min_n = min_n, max_n = max_n)
# 如果在词典之中，直接返回词向量
if word in wv_from_text.wv.vocab.keys():
return wv_from_text[word]
else:
# 不在词典的情况下
word_vec = np.zeros(word_size, dtype=np.float32)
ngrams_found = 0
ngrams_single = [ng for ng in ngrams if len(ng) == 1]
ngrams_more = [ng for ng in ngrams if len(ng) > 1]
# 先只接受2个单词长度以上的词向量
for ngram in ngrams_more:
if ngram in wv_from_text.wv.vocab.keys():
word_vec += wv_from_text[ngram]
ngrams_found += 1
#print(ngram)
# 如果，没有匹配到，那么最后是考虑单个词向量
if ngrams_found == 0:
for ngram in ngrams_single:
word_vec += wv_from_text[ngram]
ngrams_found += 1
if word_vec.any():
return word_vec / max(1, ngrams_found)
else:
raise KeyError('all ngrams for word %s absent from model' % word)
样例：
vec = wordVec('you', wv_from_text, min_n = 1, max_n = 3)
wv_from_text.most_similar(positive=[vec], topn=20)
输出为：
[-0.03046856 -0.04620409 0.00047083 0.00668182 0.08844049 0.00580471
0.03471787 0.01942951 0.06361871 0.02930737 0.07601679 0.04355551
0.06793331 0.02356228 0.05080499 -0.03055479 -0.05502079 -0.05614157
-0.11402629 -0.07533234 0.04294843 -0.01685287 -0.02865249 0.08180179
0.08751559 -0.02826789 -0.04464697 0.06841499 0.08092147 0.06479542
-0.06272016 0.00305659 -0.08449398 0.05851372 -0.05001214 -0.09615612
0.1220563 0.01333999 -0.00736996 -0.1590448 -0.0324901 -0.08581112
0.1250705 0.07985661 0.13089788 -0.08359518 -0.06823734 -0.05536203
0.00560144 0.04050508 -0.08083475 -0.01068204 -0.1067571 0.07220849
0.16063938 -0.01130883 0.01461993 0.09937483 -0.03789247 -0.04542825
0.04778216 -0.00553073 -0.0384419 -0.00123461 0.01612925 -0.03791366
-0.04620606 0.01003874 -0.01125956 -0.00247242 0.02202561 0.03858603
-0.03880063 0.09048815 0.0443082 0.12101042 -0.01660945 0.0443289
0.01217092 -0.02422529 0.0693123 0.13483234 -0.09292091 0.03101995
0.04277744 -0.10431696 0.07195818 0.03637034 -0.08749907 -0.06886439
0.07444612 0.00367155 -0.03007829 0.03526557 -0.07293753 -0.07414234
0.00711446 -0.02770442 -0.10819276 0.01971408 -0.04046911 0.06738634
0.00200529 0.12304009 -0.07979871 0.07926777 0.08503009 -0.15294099
0.00156821 0.02695789 0.00967164 -0.1532162 0.00050335 -0.07192959
0.00104416 -0.06036502 0.07250883 -0.05369675 -0.07887848 -0.02159125
0.14825755 -0.09300073 -0.0173375 0.12938067 0.0985603 -0.01668607
-0.00158964 -0.03757982 0.11439265 0.01343829 -0.01988285 0.02558876
-0.0197126 -0.00055583 -0.11743422 -0.13186961 -0.00245222 0.04263183
-0.00067508 0.01845483 0.02169867 -0.18447219 0.04835228 -0.03931556
-0.00837371 -0.07180715 -0.10068532 0.05785515 0.06896072 0.07997242
-0.0403851 -0.03716491 -0.09986192 0.03541118 0.01249392 0.0545677
-0.07183622 -0.07361015 -0.03127496 0.09413384 0.12698588 0.0743419
0.02534755 0.06182925 0.01498506 -0.03010761 -0.10066734 0.11040869
0.21788178 -0.01249244 0.04311302 0.00530825 -0.02670412 0.03442763
0.0136317 0.11100641 -0.01051623 0.00064256 -0.0282297 0.04748847
-0.00928359 0.04906752 -0.03343965 -0.07915296 0.00614052 0.03317159
-0.05871526 0.01438833 -0.1045052 -0.05423214 0.0370161 0.00888027
-0.02865865 -0.02153877 0.02579646 0.25401807 0.08651109 -0.14689459
0.00578697 0.09873005]
[('you', 1.0),
('know', 0.9304447770118713),
('just', 0.9255117177963257),
('can', 0.922150731086731),
("don't", 0.9210973978042603),
('you.', 0.9202504754066467),
('want', 0.9194904565811157),
('would', 0.9144765138626099),
('it.', 0.9142146110534668),
('see', 0.9135138988494873),
('will', 0.9130226373672485),
('your', 0.9126819372177124),
('to', 0.9122183322906494),
("i'm", 0.9122059941291809),
('when', 0.911807119846344),
('like', 0.9112892746925354),
('me.', 0.9108408689498901),
("you'll", 0.9074229001998901),
("won't", 0.9056110978126526),
('could', 0.9050869941711426)]
分词，词向量编码，去除标点符号（中英文）和数字等
import jieba
from string import punctuation
from zhon import hanzi
import re
def remove_digits_and_punctuation(input):
punc = punctuation + hanzi.punctuation + u'0123456789'
res = re.sub(r'[{}]+'.format(punc), '', input)
return res
sentences = '，。“；”笑音点亮了四面风。12345678：。'
cut_res = jieba.lcut(sentences)
print(cut_res)
embedding_res = [0] * len(sentences)
print(len(embedding_res))
i = 0
j = 0
while i < len(cut_res):
print('i: ', i, 'cut_res[i]: ', cut_res[i])
l = len(cut_res[i])
print('l: ', l)
j += l
print('j: ', j)
cut_res[i] = remove_digits_and_punctuation(cut_res[i])
print('cut_res: ', cut_res[i])
if cut_res[i]:
vec = wordVec(cut_res[i], wv_from_text, min_n = 1, max_n = 3)
else:
vec = np.array([0.0] * 200)
print('vec: ', vec)
while l > 0:
embedding_res[j - l] = vec
l -= 1
i += 1
print('embedding: ', embedding_res, 'shape: ', len(embedding_res))
输出为：
['，', '。', '“', '；', '”', '笑音', '点亮', '了', '四面', '风', '。', '12345678', '：', '。']
24
i: 0 cut_res[i]: ，
l: 1
j: 1
cut_res:
vec: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.]
i: 1 cut_res[i]: 。
l: 1
j: 2
cut_res:
vec: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.]
i: 2 cut_res[i]: “
l: 1
j: 3
cut_res:
vec: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.]
i: 3 cut_res[i]: ；
l: 1
j: 4
cut_res:
vec: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.]
i: 4 cut_res[i]: ”
l: 1
j: 5
cut_res:
vec: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.]
i: 5 cut_res[i]: 笑音
l: 2
j: 7
cut_res: 笑音
vec: [ 5.10192811e-02 8.64458084e-02 2.72869766e-02 6.81001991e-02
-4.12502810e-02 -2.48831627e-03 7.15994760e-02 -7.54914433e-02
8.72121900e-02 -5.08092046e-02 3.79315065e-03 3.01644672e-02
6.21919446e-02 -8.28784630e-02 6.01179190e-02 -3.48375998e-02
3.59368138e-02 6.63799047e-02 9.83055085e-02 -6.23020306e-02
3.17706876e-02 -2.03447621e-02 6.47675768e-02 5.07080406e-02
6.15553707e-02 -8.77428949e-02 -1.36797538e-03 2.84378324e-02
-1.94962323e-03 -8.35532919e-02 5.80319203e-02 -7.96852708e-02
3.15394849e-02 -7.41659524e-03 -3.77643816e-02 4.82260622e-02
-2.33785773e-04 -2.28523258e-02 -3.13052302e-03 -4.01451960e-02
-2.34025195e-02 -9.59131960e-03 1.64872948e-02 -1.02314368e-01
1.21370494e-01 -2.27488577e-01 -6.70439377e-02 9.18782782e-03
7.70150423e-02 -1.69236481e-03 1.14916503e-01 2.17575673e-02
6.18905574e-02 -1.68631837e-01 1.55577855e-02 6.75908476e-02
2.01813914e-02 9.42285731e-02 -5.24696484e-02 3.48458141e-02
1.53833842e-02 7.03357160e-02 3.34804207e-02 -7.09908307e-02
8.20670202e-02 4.82626781e-02 -7.95578137e-02 2.40238346e-02
4.51039895e-02 -2.41496470e-02 -6.30592480e-02 -3.62457149e-02
1.02091148e-01 1.39172480e-01 9.85928103e-02 -9.20496229e-03
-1.04304599e-02 -4.95454483e-02 -6.97768405e-02 1.11088142e-01
9.88610983e-02 1.05012767e-01 -1.23106755e-01 8.31239820e-02
2.79275402e-02 -3.40252183e-02 -3.53068151e-02 9.10511017e-02
3.81451054e-03 -5.89959286e-02 5.43704480e-02 -6.76938966e-02
-5.79511710e-02 -4.04677056e-02 1.87169462e-02 -8.80567282e-02
-2.05466244e-02 -1.48815438e-04 -1.71958581e-01 -2.32318752e-02
4.65989485e-02 -1.19610997e-02 4.57616858e-02 1.84086114e-01
-5.48448265e-02 7.80623779e-02 7.66890049e-02 -7.67075494e-02
-1.90922692e-02 -2.39461400e-02 -2.73862649e-02 2.84310244e-02
1.20883202e-02 1.09741762e-01 6.17384538e-02 -1.14707597e-01
-2.54554208e-02 3.00694034e-02 4.33069393e-02 -5.84166311e-02
1.01415133e-02 2.95792986e-02 9.86946840e-03 8.85019973e-02
2.45444532e-02 1.36519849e-01 3.32900621e-02 9.07757729e-02
1.27771422e-01 3.10493796e-03 3.69252414e-02 -6.91801682e-02
-2.93492675e-02 -6.89163357e-02 -1.64239202e-02 5.75249158e-02
-1.35873899e-01 2.01466531e-02 -3.86626758e-02 -6.68831542e-02
2.78472658e-02 -7.96392635e-02 -1.41688272e-01 -2.93351840e-02
-1.25463143e-01 -1.29742190e-01 -3.56145389e-02 6.85161329e-04
5.78199625e-02 2.98947673e-02 -1.33773103e-01 -2.54216213e-02
-4.26123850e-02 -6.05049767e-02 2.03881841e-02 -3.58426943e-02
-7.31770545e-02 5.98782627e-03 3.37447226e-02 1.70858447e-02
1.14040740e-01 4.42883223e-02 9.93460417e-02 5.03132306e-02
2.42930651e-02 5.08946478e-02 -3.58870551e-02 8.09912831e-02
3.04381549e-02 -1.67788230e-02 -2.92819017e-03 4.10458334e-02
-7.30287135e-02 6.82825819e-02 -6.31240308e-02 6.11354485e-02
8.49360600e-02 -1.13950849e-01 1.41176790e-01 3.83103527e-02
1.46546125e-01 7.37173930e-02 -7.22329915e-02 -1.34129420e-01
-4.54541966e-02 1.37862712e-01 -1.02958918e-01 6.53435886e-02
3.16901761e-03 -5.46582192e-02 6.59367442e-02 -5.80415390e-02
8.15996807e-03 -1.54688256e-02 7.64988810e-02 1.65079266e-01
1.07933450e-02 -2.64461935e-02 -3.98700982e-02 4.92776297e-02]
i: 6 cut_res[i]: 点亮
l: 2
j: 9
cut_res: 点亮
vec: [-0.01768023 -0.02101073 0.00222622 0.11829606 -0.06718969 -0.06321988
-0.03018856 0.019834 0.14403397 0.13341312 0.12650095 0.03922224
-0.02940177 0.08564904 0.03093209 -0.08279988 0.05251455 0.01312255
-0.00912252 -0.09084655 -0.05698389 -0.01561224 0.01393828 -0.00353072
0.07021628 0.06099621 0.06974492 0.01380002 -0.06035152 -0.07106145
-0.01192662 0.02808318 0.00893946 0.07817334 -0.07683556 0.01354348
0.08718141 -0.08419169 -0.153062 0.0079189 0.08603745 0.09027201
0.13700888 -0.01806608 0.01138203 -0.12208204 -0.01612252 -0.11951811
-0.02651472 0.07316632 -0.13655363 -0.09080917 -0.06847141 0.00921623
0.08639615 0.02395411 -0.06131881 0.03691281 -0.00421049 -0.09100683
0.06367434 0.09271407 0.01355449 -0.03757593 -0.01291312 -0.01683506
-0.14582928 0.06681589 -0.03641405 -0.08877677 -0.02108857 -0.04781605
0.10211106 0.07410366 -0.11586935 0.11245692 0.02352474 -0.0324747
0.00743704 -0.01989724 0.06908717 0.04335336 0.00744217 0.17175
-0.03369368 -0.13685295 -0.04191368 0.01009059 -0.07856917 -0.03990303
-0.02321955 -0.02555126 -0.03539938 -0.20781532 -0.09387314 -0.06147756
0.01242051 0.02123528 -0.03701855 -0.02203871 0.07091475 -0.04225369
-0.05694779 0.15341891 -0.01977178 0.07510348 0.02166977 -0.09155449
0.03142521 0.00403793 0.06517214 -0.00628156 -0.0028512 0.01807606
-0.03411818 0.05281923 0.11822462 -0.00337326 -0.08570051 -0.04044199
0.02450689 -0.0850914 0.02618879 0.08928602 0.09134506 0.07105121
0.06478912 -0.05809508 0.07550212 0.02656618 0.09533101 0.01940514
0.02902284 -0.10132863 0.04818781 0.02809676 -0.08263936 -0.03839678
-0.03498 -0.02844471 0.01208689 -0.10620916 -0.15129665 0.01371144
-0.00457611 -0.00490819 -0.00169162 0.06566527 0.05889647 -0.02516132
-0.08777542 -0.0095035 -0.19625175 -0.06258978 0.06075529 -0.04033036
-0.07725903 0.01152029 -0.09447431 -0.05991882 0.12250321 0.02133923
0.0536708 0.10829558 0.02410773 0.0664623 -0.10994676 0.01619677
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i: 7 cut_res[i]: 了
l: 1
j: 10
cut_res: 了
vec: [ 0.10552851 -0.11784042 0.04319089 0.11526939 0.1073673 0.01010414
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i: 8 cut_res[i]: 四面
l: 2
j: 12
cut_res: 四面
vec: [ 0.05601809 0.00323394 -0.00518583 0.12160996 -0.1279734 0.02088064
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i: 9 cut_res[i]: 风
l: 1
j: 13
cut_res: 风
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i: 10 cut_res[i]: 。
l: 1
j: 14
cut_res:
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i: 11 cut_res[i]: 12345678
l: 8
j: 22
cut_res:
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i: 12 cut_res[i]: ：
l: 1
j: 23
cut_res:
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i: 13 cut_res[i]: 。
l: 1
j: 24
cut_res:
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0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
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0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])] shape: 24
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