2. The method of claim 1 , wherein: the trained neural network system has been trained to receive a current sequence of words authored by a current author and to generate a respective word score for each word in a pre-determined set of words, wherein each of the respective word scores represents a predicted likelihood that the corresponding word follows a last word in the current sequence of words, and determining the author vector for the first author using the trained neural network system comprises iteratively providing each of the plurality of first sequences of words to the trained neural network system to determine the author vector for the first author using gradient descent.
2. The method of claim 1 , wherein: the trained neural network system has been trained to receive a current sequence of words authored by a current author and to generate a respective word score for each word in a pre-determined set of words, wherein each of the respective word scores represents a predicted likelihood that the corresponding word follows a last word in the current sequence of words, and determining the author vector for the first author using the trained neural network system comprises iteratively providing each of the plurality of first sequences of words to the trained neural network system to determine the author vector for the first author using gradient descent. 3. The method of claim 2 , wherein the trained neural network system comprises an embedding layer configured to map an identifier for the current author and each word in the current sequence of words to respective vector representations, a combining layer configured to combine the vector representations into a combined representation, and a classifier layer configured to generate the word scores using the combined representation.