BibTeX
@MISC{_learningto,
author = {},
title = {Learning to Classify Email into "Speech Acts"},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
It is often useful to classify email according to the intent of the sender (e.g., "propose a meeting", "deliver information"). We present experimental results in learning to classify email in this fashion, where each class corresponds to a verbnoun pair taken from a predefined ontology describing typical “email speech acts”. We demonstrate that, although this categorization problem is quite different from “topical ” text classification, certain categories of messages can nonetheless be detected with high precision (above 80%) and reasonable recall (above 50%) using existing text-classification learning methods. This result suggests that useful task-tracking tools could be constructed based on automatic classification into this taxonomy. 1
Keyphrases
speech act quot predefined ontology certain category useful task-tracking tool categorization problem information quot typical email speech act present experimental result verbnoun pair topical text classification text-classification learning method reasonable recall automatic classification meeting quot high precision
