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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-gram does the opposite, and tries to predict several context words from a single input word.
Intuitively, the first task is much simpler, this implies a much ...
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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 actual representation or just an approximation of it. But in any case its not very reliable outside from some edge cases.
If we picked out only a single ...
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Your question is really broad.. But there are many manuals and tutorials which describe how to do it step by step.
My personal favorite would be:
Practical Text Classification With Python and Keras - It has a very detailed explanation of every step of the implementation while remaining practical.
You can also try:
A Word2Vec Keras tutorial - This one ...
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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 the exact same context of some word $e_k$ with 3 other context words as well. An example would be:
He leaped over the truck
He jumped over the truck
Where ...
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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 different embedding in a sentence containing "hit the ball" vs. a sentence containing "flies and eats bugs". On top of that, Google has released a ...
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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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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 has two models, namely
Continuous Bag of Word (CBOW) and
Skip Gram Model.
Both of these methods use the concept of a neural network that maps words to target ...
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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 without listing them in your dictionary. Or use regular expressions to recognise ID numbers. ML is not really the right tool here.
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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 tasks. You don't, however, do any prediction directly from the skip-gram model.
To maximize the first term, co-occuring words must have large inner products. ...
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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 words are $d$-dimensional vectors, then each word's position in this vector space says something about what that word means. This is because word2vec learns to ...
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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://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
After representing word as vectors, you feed your text to LSTM in a deep architecture which the last ...
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