例如我们使用 gensim
训练一个 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)
当我们查询单词之间的相似度时,我们发现负相似度分数:
>>> 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
我们如何解释负分?
如果是余弦相似度,范围不应该是[0,1]
吗?
Word2Vec.similarity(x,y)
函数的上限和下限是多少? 文档里写的不多:https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec.similarity =(
查看 Python 包装器代码,没有太多:https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/models/word2vec.py#L1165
(如果可能,请给我指出实现相似度函数的.pyx
代码。)
最佳答案
余弦相似度范围为 -1 到 1,与正余弦波相同。
至于来源:
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])
关于python - 从 gensim 解释负 Word2Vec 相似度,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/42381902/