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我正在使用 gensim 加载预训练的 fasttext 模型。我从 fasttext网站下载了英文维基百科训练模型。

这是我为加载预训练模型而编写的代码：

from gensim.models import FastText as ft
model=ft.load_fasttext_format("wiki.en.bin")

我尝试检查人声中是否存在以下短语（这种情况很少见，因为这些是预先训练的模型）。

print("internal executive" in model.wv.vocab)
print("internal executive" in model.wv)

False
True

所以词汇表中没有“内部执行”这个短语，但我们仍然有与之对应的词向量。

model.wv["internal executive"]
Out[46]:
array([ 0.0210917 , -0.15233646, -0.1173932 , -0.06210957, -0.07288644,
       -0.06304111,  0.07833624, -0.17026938, -0.21922196,  0.01146349,
       -0.13639058,  0.17283678, -0.09251394, -0.17875175,  0.01339212,
       -0.26683623,  0.05487974, -0.11843193, -0.01982722,  0.37037706,
       -0.24370994,  0.14269598, -0.16363597,  0.00328478, -0.16560239,
       -0.1450972 , -0.24787527, -0.01318423,  0.03277111,  0.16175713,
       -0.19367714,  0.16955379,  0.1972683 ,  0.09044111,  0.01731548,
       -0.0034324 , -0.04834719,  0.14321515,  0.01422525, -0.08803893,
       -0.29411593, -0.1033244 ,  0.06278021,  0.16452256,  0.0650492 ,
        0.1506474 , -0.14194389,  0.10778475,  0.16008648, -0.07853138,
        0.2183501 , -0.25451994, -0.0345991 , -0.28843886,  0.19964759,
       -0.10923116,  0.26665714, -0.02544454,  0.30637854,  0.04568949,
       -0.04798719, -0.05769338,  0.25762403, -0.05158515, -0.04426906,
       -0.19901046,  0.00894193, -0.17269588, -0.24747233, -0.19061406,
        0.14322804, -0.10804397,  0.4002605 ,  0.01409482, -0.04675362,
        0.10039093,  0.07260711, -0.0938239 , -0.20434211,  0.05741301,
        0.07592541, -0.02921724,  0.21137556, -0.23188967, -0.23164661,
       -0.4569614 ,  0.07434579,  0.10841205, -0.06514647,  0.01220404,
        0.02679767,  0.11840229,  0.2247431 , -0.1946325 , -0.0990666 ,
       -0.02524677,  0.0801085 ,  0.02437297,  0.00674876,  0.02088535,
        0.21464555, -0.16240154,  0.20670174, -0.21640894,  0.03900698,
        0.21772243,  0.01954809,  0.04541844,  0.18990673,  0.11806394,
       -0.21336791, -0.10871669, -0.02197789, -0.13249406, -0.20440844,
        0.1967368 ,  0.09804545,  0.1440366 , -0.08401451, -0.03715726,
        0.27826542, -0.25195453, -0.16737154,  0.3561183 , -0.15756823,
        0.06724873, -0.295487  ,  0.28395334, -0.04908851,  0.09448399,
        0.10877471, -0.05020981, -0.24595442, -0.02822314,  0.17862654,
        0.06452435, -0.15105674, -0.31911567,  0.08166212,  0.2634299 ,
        0.17043628,  0.10063848,  0.0687021 , -0.12210461,  0.10803893,
        0.13644943,  0.10755012, -0.09816817,  0.11873955, -0.03881042,
        0.18548298, -0.04769253, -0.01511982, -0.08552645, -0.05218676,
        0.05387992,  0.0497043 ,  0.06922272, -0.0089245 ,  0.24790663,
        0.27209425, -0.04925154, -0.08621719,  0.15918174,  0.25831223,
        0.01654229, -0.03617229, -0.13490392,  0.08033483,  0.34922174,
       -0.01744722, -0.16894792, -0.10506647,  0.21708378, -0.22582002,
        0.15625793, -0.10860757, -0.06058934, -0.25798836, -0.20142137,
       -0.06613475, -0.08779443, -0.10732629,  0.05967236, -0.02455976,
        0.2229451 , -0.19476262, -0.2720119 ,  0.03687386, -0.01220259,
        0.07704347, -0.1674307 ,  0.2400516 ,  0.07338555, -0.2000631 ,
        0.13897157, -0.04637206, -0.00874449, -0.32827383, -0.03435039,
        0.41587186,  0.04643605,  0.03352945, -0.13700874,  0.16430037,
       -0.13630766, -0.18546128, -0.04692861,  0.37308362, -0.30846512,
        0.5535561 , -0.11573419,  0.2332801 , -0.07236694, -0.01018955,
        0.05936847,  0.25877884, -0.2959846 , -0.13610311,  0.10905041,
       -0.18220575,  0.06902339, -0.10624941,  0.33002165, -0.12087796,
        0.06742091,  0.20762768, -0.34141317,  0.0884434 ,  0.11247049,
        0.14748637,  0.13261876, -0.07357208, -0.11968047, -0.22124515,
        0.12290633,  0.16602683,  0.01055585,  0.04445777, -0.11142147,
        0.00004863,  0.22543314, -0.14342701, -0.23209116, -0.00003538,
        0.19272381, -0.13767233,  0.04850799, -0.281997  ,  0.10343244,
        0.16510887,  0.08671653, -0.24125539,  0.01201926,  0.0995285 ,
        0.09807415, -0.06764816, -0.0206733 ,  0.04697794,  0.02000999,
        0.05817033,  0.10478792,  0.0974884 , -0.01756372, -0.2466861 ,
        0.02877498,  0.02499748, -0.00370895, -0.04728201,  0.00107118,
       -0.21848503,  0.2033032 , -0.00076264,  0.03828803, -0.2929495 ,
       -0.18218371,  0.00628893,  0.20586628,  0.2410889 ,  0.02364616,
       -0.05220835, -0.07040054, -0.03744286, -0.06718048,  0.19264086,
       -0.06490505,  0.27364203,  0.05527219, -0.27494466,  0.22256687,
        0.10330909, -0.3076979 ,  0.04852265,  0.07411488,  0.23980476,
        0.1590279 , -0.26712465,  0.07580928,  0.05644221, -0.18824042],

现在我的困惑是 Fastext 也为单词的字符 ngram 创建向量。因此，对于“内部”单词，它将为其所有字符 ngram 创建向量，包括整个单词，然后该单词的最终单词向量是其字符 ngram 的总和。

但是，它怎么还能给我一个单词甚至整个句子的向量呢？fasttext 向量不是用于单词及其 ngram 吗？那么当它显然是两个词时，我看到的这些向量是什么？

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1 回答 1

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来自论文Enriching Word Vectors with Subword Information：

在大型未标记语料库上训练的连续词表示对于许多自然语言处理任务很有用。学习这种表示的流行模型通过为每个单词分配一个不同的向量来忽略单词的形态。这是一个限制，尤其是对于具有大量词汇和许多生僻词的语言。在本文中，我们提出了一种基于 skipgram 模型的新方法，其中每个单词都表示为一袋字符 n-gram。向量表示与每个字符 n-gram 相关联；单词被表示为这些表示的总和。

因此，词汇外的单词被表示为字符 ngram 向量的总和。虽然目的是处理像“blargfizzle”这样的词汇外单词（unks），但它也处理像您输入的短语。

如果您查看Gensim中向量的实现，您会发现这确实是它正在做的事情（以及规范化和散列等） - 我添加了一些以 XXX 开头的注释：

def word_vec(self, word, use_norm=False):
    """
    Accept a single word as input.
    Returns the word's representations in vector space, as a 1D numpy array.
    If `use_norm` is True, returns the normalized word vector.
    """
    if word in self.vocab:
        # XXX in-vocab terms return with a simple lookup
        return super(FastTextKeyedVectors, self).word_vec(word, use_norm)
    else:
        # from gensim.models.fasttext import compute_ngrams
        # XXX Initialize the vector for the unk
        word_vec = np.zeros(self.vectors_ngrams.shape[1], dtype=np.float32)
        ngrams = _compute_ngrams(word, self.min_n, self.max_n)
        if use_norm:
            ngram_weights = self.vectors_ngrams_norm
        else:
            ngram_weights = self.vectors_ngrams
        ngrams_found = 0
        for ngram in ngrams:
            ngram_hash = _ft_hash(ngram) % self.bucket
            if ngram_hash in self.hash2index:
                # XXX add the vector for the ngram to the unk vector
                word_vec += ngram_weights[self.hash2index[ngram_hash]]
                ngrams_found += 1
        if word_vec.any():
            return word_vec / max(1, ngrams_found)
        else:  # No ngrams of the word are present in self.ngrams
            raise KeyError('all ngrams for word %s absent from model' % word)

请注意，这并不意味着它可以为任意字符串提供向量 - 它仍然需要至少有一些 unk 中的 ngram 的数据，因此如果您的向量接受过训练，类似xwkxwkzrwor的字符串天爾遠波可能无法返回任何内容英语。

于 2018-06-13T02:56:58.373 回答