26
votes
What are the main differences between skip-gram and continuous bag of words?
So as you're probably already aware of, CBOW and Skip-gram are just mirrored versions of each other. CBOW is trained to predict a single word from a fixed window size of context words, whereas Skip-...
- 5,428
2
votes
What are the main differences between skip-gram and continuous bag of words?
Word embeddings are the results of learning from deep learning algorithms, which can learn characters from data through feature extraction. One implementation of word embedding is word2vec.
Word2vec ...
- 21
1
vote
Accepted
Dot notation for matrix element index in formula of word2vec embedding calculation
The notation $W_{k, \cdot}$ likely refers to the $k$-th row of $W$. The dot just suggests taking all the elements in the row. It is just a convenient way to index an entire row of a matrix.
Similar ...
- 136
1
vote
What is the meaning of "continuous" in a continuous bag-of-words model?
A bag-of-words-model (BOW) is usually used to represent a text: you throw all the words together (as if in a bag), without keeping track of their sequence. This is a gross simplification over a text, ...
- 5,417
1
vote
Is my interpretation of the mathematics of the CBOW and Skip-Gram models correct?
The following figure from this article can be helpful:
This figure represents "Skip-Gram model structure. Current center word is 'passes'".
- 1,836
Only top scored, non community-wiki answers of a minimum length are eligible