從gensim解釋否定的Word2Vec相似度

Question

E.g.我們培養使用gensim一個word2vec模型：

from gensim import corpora, models, similarities 
from gensim.models.word2vec import Word2Vec 

documents = ["Human machine interface for lab abc computer applications", 
       "A survey of user opinion of computer system response time", 
       "The EPS user interface management system", 
       "System and human system engineering testing of EPS", 
       "Relation of user perceived response time to error measurement", 
       "The generation of random binary unordered trees", 
       "The intersection graph of paths in trees", 
       "Graph minors IV Widths of trees and well quasi ordering", 
       "Graph minors A survey"] 

texts = [[word for word in document.lower().split()] for document in documents] 
w2v_model = Word2Vec(texts, size=500, window=5, min_count=1) 

當我們查詢詞之間的相似性，我們發現負的相似性指標：

>>> w2v_model.similarity('graph', 'computer') 
0.046929569156789336 
>>> w2v_model.similarity('graph', 'system') 
0.063683518562347399 
>>> w2v_model.similarity('survey', 'generation') 
-0.040026775040430063 
>>> w2v_model.similarity('graph', 'trees') 
-0.0072684112978664561 

我們如何解釋負分？

如果是餘弦相似度不應該是[0,1]？

Word2Vec.similarity(x,y)函數的上界和下界是什麼？沒有多少寫在文檔：https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec.similarity =（

瞭解一下Python包裝代碼，沒有太：https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/models/word2vec.py#L1165

（如果可能的話，請你點我的.pyx代碼其中相似性函數被實現。）

Answer 1

餘弦相似性的範圍從-1到1，同樣作爲常規餘弦波。

至於源：

https://github.com/RaRe-Technologies/gensim/blob/ba1ce894a5192fc493a865c535202695bb3c0424/gensim/models/word2vec.py#L1511

def similarity(self, w1, w2): 
    """ 
    Compute cosine similarity between two words. 
    Example:: 
     >>> trained_model.similarity('woman', 'man') 
     0.73723527 
     >>> trained_model.similarity('woman', 'woman') 
     1.0 
    """ 
    return dot(matutils.unitvec(self[w1]), matutils.unitvec(self[w2]) 

Answer 2

正如其他人所說的餘弦相似性的範圍可以從-1到基於兩個向量之間的角度1進行比較。 gensim中的確切實現是歸一化向量的簡單點積。

https://github.com/RaRe-Technologies/gensim/blob/4f0e2ae0531d67cee8d3e06636e82298cb554b04/gensim/models/keyedvectors.py#L581

def similarity(self, w1, w2): 
     """ 
     Compute cosine similarity between two words. 
     Example:: 
      >>> trained_model.similarity('woman', 'man') 
      0.73723527 
      >>> trained_model.similarity('woman', 'woman') 
      1.0 
     """ 
     return dot(matutils.unitvec(self[w1]), matutils.unitvec(self[w2])) 

在演繹方面，你能想到這些值的像你想象的相關係數。值1是單詞向量（例如，「女人」與「女人」相比）的完美關係，值0表示單詞之間不存在關係，並且值-1表示單詞之間完全相反的關係。