Senders, Receivers and Authors in Document Classification

In many document classification problems, sets of people will be associated with the document. These sets might include document authors, or people who have read the document, or the sender of an electronic message, or the recipients of the message, or those carbon copied, or those blind carbon copied. It is obvious that these sets of people can constitute important information that can help to classify the document. In this paper, we propose a simple method for mapping the set of people in a sender or receiver category to a single, low dimensional vector in a latent space. There are many ways that this vector can be used to help with the document classification task, and in the paper we consider three distinct possibilities in detail. We find that mapping a set of senders or receivers to a latent space in this way and incorporating this mapping into a classifier can greatly boost classification accuracy on several real electronic discovery tasks.