Output a word instead of a vector after word embedding?
It depends on the architecture of the model. 

A lot of work uses the text data to classify some specific category (e.g., the IMDB movie reviews or sentiment analysis on Tweets). 

Other work can actually take the words of a sentence and predict the last word. Or break it into each word predicting the subsequent word, which is really what the RNN/embedding dimension is doing.

Other's have made these even at the character level. Ultimately, it depends on how you process the data and specify your outcome. Below are some useful tutorials that should add a little more clarity.


http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
http://deeplearning.net/tutorial/lstm.html
http://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/
https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py