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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. 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| CopyRight 2018-2019 实验室设备网 版权所有 |