Mining E-mail to Improve Information Literacy Instruction

Mining E-mail Authorship

In this paper we report an investigation into the learning of authorship identification or categorisation for the case of e-mail documents. We use various e-mail document features such as structural characteristics and linguistic evidence together with the Support Vector Machine as the learning algorithm. Experiments on a number of e-mail documents give promising results with some e-mail docume...

Exploiting E-mail Structure to Improve Summarization

This paper presents the design and implementation of a system to summarize e-mail messages. The system exploits two aspects of e-mail, thread reply chains and commonly-found features, to generate summaries. The system uses existing software designed to summarize single text documents. Such software typically performs best on well-authored, formal documents. E-mail messages, however, are typical...

E-Mail Worm Detection Using Data Mining

This work applies data mining techniques to detect e-mail worms. E-mail messages contain a number of different features such as the total number of words in message body/subject, presence/absence of binary attachments, type of attachments, and so on. The goal is to obtain an efficient classification model based on these features. The solution consists of several steps. First, the number of feat...

Assessment of library instruction to develop student information literacy skills.

To the instruction cave, librarian!: Graphic novels and information literacy