Consider the task of predicting the POS (parts of speech) tag of a word using a model similar to the one we discussed in class predicting the language of documents. The task consists of tagging each word in a sequence of words as either Noun, Verb, Adjective, ... (these labels are known as "tags"). Assume the tagset is of dimension T (there are T distinct tags). What would be the dimension of the task-specific embedding of words the model would learn? What would be the interpretation of an embedding vector for a given word w?