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Cosine similarity is used to represent the similarity between vectors in word2vec. Keep in mind that the similarity does depend on the specific dataset on which word2vec was trained.
- From Quora: (https://www.quora.com/What%E2%80%99s-the-opposite-of-word-%E2%80%9CRussia%E2%80%9D-vector-representations-of-words-word2vec-NLP-1)
"Word2vec does not capture antonyms. The vectors captures the word-context from the trained corpus. So it highly depends on what corpus are you training your word2vec model. In word2vec the cosine similarly of antonyms are generally high. The cosine similarity of (fast,slow) (high, low) should be quite high because they occur in the same context.
If you want to find the antonyms of words you may use word net."
- https://www.distilled.net/resources/a-beginners-guide-to-word2vec-aka-whats-the-opposite-of-canada/
- From the paper "Neural Word Embedding as Implicit Matrix Factorization":
"When representing words, there is some intuition behind ignoring negative values: humans can easily think of positive associations (e.g. “Canada” and “snow”) but find it much harder to invent negative ones (“Canada” and “desert”). This suggests that the perceived similarity of two words is more influenced by the positive context they share than by the negative context they share. It therefore makes some intuitive sense to discard the negatively associated contexts and mark them as “uninformative” (0) instead. Indeed, it was shown that the PPMI metric performs very well on semantic similarity tasks [5]."
Reading on "Word Translation without Parallel Data":
- Word Translation without Parallel Data:
https://arxiv.org/pdf/1710.04087.pdf
Reading on word2vec:
- https://code.google.com/archive/p/word2vec/
- https://www.tensorflow.org/tutorials/word2vec
- https://deeplearning4j.org/word2vec
- Distributed Representations of Words and Phrases and their Compositionality:
https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
- Efficient Estimation of Word Representations in Vector Space:
https://arxiv.org/pdf/1301.3781.pdf
- Measuring Word Significance using Distributed Representations of Words:
https://arxiv.org/pdf/1508.02297v1.pdf
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