19
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-...
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2
votes
Accepted
How does Continuous Bag of Words ensure that similar words are encoded as similar embeddings?
Unlike in skip-gram, the reason similar words have similar embeddings in CBOW is because the words show up in the same contexts of other skipped words.
lets assume two words $e_i$ and $e_j$ pop up in ...
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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
2
votes
Do individual dimensions in vector space have meaning?
Do individual dimensions in vector space have meaning?
IIRC, some dimensions are interpretable, but in general this is not the case. Also it is debatable as to wether it is actually learning the ...
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1
vote
Should I need to use BERT embeddings while tokenizing using BERT tokenizer?
Generally speaking, the power of BERT for applications like NER is that the authors (of whichever implementation you use) performed a large-scale pretraining effort to create the embeddings. You can ...
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1
vote
How would one disambiguate between two meanings of the same word in a sentence?
I'd suggest BERT for this. It is essentially a word-embedding model that uses at local context to determine the appropriate embedding for each word. This means it would assign "bat" a ...
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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'".
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vote
How do I classify strings with possibly no meaning?
I would just use a dictionary. A simple list lookup would tell you whether it's a recognised word or not. As an added bonus you can add some basic language processing, eg to identify inflected forms ...
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vote
Skip-Gram Model Training
Almost, but no. When you maximize that objective function, you do so by adjusting the parameters $\phi$ and $\theta$. After you're done with training, you can use your word embeddings for other NLP ...
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1
vote
What do the vectors of the center and outside word look like in word2vec?
No, the word vectors are not one-hot encodings. Yes, they are learned.
The purpose of the word2vec model is actually to learn dense, semantically meaningful encodings for words. That is, if your ...
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1
vote
How should the output layer of an LSTM be when the output are word embeddings?
Actually, LSTM is not used to get word2vec. Indeed, word2vec is extracted from corpus of words using MLP (Multi Layer Perceptron). There is a great tutorial on how to extract word2wec:
http://...
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