Help me understand how word-as-vector representations are constructed Let's suppose I have a big list of words. I want to turn this list into a vector space of dimension $N$ such that each word is a vector in this vector space. But I have no idea how to go about with that. Some questions:


Is the list enough? For each element of the list, do I need $x$ example sentences also?
How does the computer deduce the dimensions of the vector space from the list/corpus?
Is there a way to figure out whether the dimensions of the vector space correspond to something in English?
I am assuming that you mean a vector representation of words, not to be confused by the vector representation produced in a bag of words approach that represent a document in vector space. Word2vec is an approach in which you train a model to represent words as a function of the provided context. 

The answers that follow are: 


1) No: You'll need some representation of context in which the word is used. For example: Skip-grams.
2) No: That is a user defined parameter
3) &lt; I do not understand this question >