Combining textual and non-textual features for e-mail importance estimation

In this work, we present a binary classification problem in which we aim to identify those email messages that the receiver will reply to. The future goal is to develop a tool that informs a knowledge worker which emails are likely to need a reply. The Enron corpus was used to extract training examples. We analysed the word n-grams that...

Profiling knowledge workers using open online profiles

In this paper we investigate the accuracy of user terminology models extracted from open online profiles. In our project, user models are used for the profiling of knowledge workers in order to assist them with information tasks such as email filtering and professional search. We created terminology models from profiles on LinkedIn, Twitter and...