Exploring the traits of manual e-mail categorization text patterns

Abstract

Automated e-mail answering with a standard answer is a text categorization task. Text categorization by matching manual text patterns to messages yields good performance if the text categories are specific. Given that manual text patterns embody informal human perception of important wording in a written inquiry, it is interesting to investigate more formal traits of this important wording, such as the amount of matching text, distance between matching words, n-grams, part-of-speech patterns, and vocabulary in the matching words. Understanding these features may help us better design textpattern extraction algorithms.