python - 为什么 FastText 词嵌入可以从另一种语言生成词的表示？

Question

最近，我从Sentiment140训练了一个 FastText 词嵌入来获得英语单词的表示。但是，今天只是为了试用，我在几个中文单词上运行了 FastText 模块，例如：

import gensim.models as gs

path = r'\data\word2vec'

w2v = gs.FastText.load(os.path.join(path, 'fasttext_model'))

w2v.wv['哈哈哈哈']

它输出：

array([ 0.00303676,  0.02088235, -0.00815559,  0.00484574, -0.03576371,
       -0.02178247, -0.05090654,  0.03063928, -0.05999983,  0.04547168,
       -0.01778449, -0.02716631, -0.03326027, -0.00078981,  0.0168153 ,
        0.00773436,  0.01966593, -0.00756055,  0.02175765, -0.0050137 ,
        0.00241255, -0.03810823, -0.03386266,  0.01231019, -0.00621936,
       -0.00252419,  0.02280569,  0.00992453,  0.02770403,  0.00233192,
        0.0008545 , -0.01462698,  0.00454278,  0.0381292 , -0.02945416,
       -0.00305543, -0.00690968,  0.00144188,  0.00424266,  0.00391074,
        0.01969502,  0.02517333,  0.00875261,  0.02937791,  0.03234404,
       -0.01116276, -0.00362578,  0.00483239, -0.02257918,  0.00123061,
        0.00324584,  0.00432153,  0.01332884,  0.03186348, -0.04119627,
        0.01329033,  0.01382102, -0.01637722,  0.01464139,  0.02203292,
        0.0312229 ,  0.00636201, -0.00044287, -0.00489291,  0.0210293 ,
       -0.00379244, -0.01577058,  0.02185207,  0.02576622, -0.0054543 ,
       -0.03115215, -0.00337738, -0.01589811, -0.01608399, -0.0141606 ,
        0.0508234 ,  0.00775024,  0.00352813,  0.00573649, -0.02131752,
        0.01166397,  0.00940598,  0.04075769, -0.04704212,  0.0101376 ,
        0.01208556,  0.00402935,  0.0093914 ,  0.00136144,  0.03284211,
        0.01000613, -0.00563702,  0.00847146,  0.03236216, -0.01626745,
        0.04095127,  0.02858841,  0.0248084 ,  0.00455458,  0.01467448],
      dtype=float32)

因此，我真的很想知道为什么从 Sentiment140 训练的FastText 模块可以做到这一点。谢谢！

Answer 1