14. A method for assigning points in a high dimensional space to each word in a vocabulary of words, the method comprising: obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein the embedding function receives a plurality of words surrounding an unknown word in a sequence of words and maps the plurality of words into a numeric representation in accordance with a set of embedding function parameters, wherein the classifier processes the numeric representation of the sequence of words to generate a respective word score for each word in a pre-determined set of words, and wherein each of the respective word scores measure a predicted likelihood that the corresponding word is the unknown word, and wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space.
14. A method for assigning points in a high dimensional space to each word in a vocabulary of words, the method comprising: obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein the embedding function receives a plurality of words surrounding an unknown word in a sequence of words and maps the plurality of words into a numeric representation in accordance with a set of embedding function parameters, wherein the classifier processes the numeric representation of the sequence of words to generate a respective word score for each word in a pre-determined set of words, and wherein each of the respective word scores measure a predicted likelihood that the corresponding word is the unknown word, and wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space. 18. The method of claim 14 , wherein training the classifier and the embedding function comprises preforming a backpropagation training technique to obtain the trained values of the embedding function parameters.