1. A method comprising: obtaining a set of first word sequences, wherein the set of first word sequences comprises a plurality of first sequences of words and, for each first sequence of words, a word that follows a last word in the first sequence of words, wherein each first word sequence in the set has been classified as being authored by a first author; processing the plurality of first sequences of words using a trained neural network system to determine an author vector for the first author, wherein the author vector characterizes the first author; after determining the author vector for the first author, obtaining a second word sequence that was authored by the first author; and using the author vector for the first author to predict a next word that follows a last word in the second word sequence.
1. A method comprising: obtaining a set of first word sequences, wherein the set of first word sequences comprises a plurality of first sequences of words and, for each first sequence of words, a word that follows a last word in the first sequence of words, wherein each first word sequence in the set has been classified as being authored by a first author; processing the plurality of first sequences of words using a trained neural network system to determine an author vector for the first author, wherein the author vector characterizes the first author; after determining the author vector for the first author, obtaining a second word sequence that was authored by the first author; and using the author vector for the first author to predict a next word that follows a last word in the second word sequence. 10. The method of claim 1 , wherein each of the plurality of first sequences of words contains a fixed number of words.