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Learning to classify email into speech acts (2004)
| Venue: | In Proceedings of Empirical Methods in Natural Language Processing |
| Citations: | 62 - 8 self |
BibTeX
@INPROCEEDINGS{Cohen04learningto,
author = {William W. Cohen and Vitor R. Carvalho and Tom M. Mitchell},
title = {Learning to classify email into speech acts},
booktitle = {In Proceedings of Empirical Methods in Natural Language Processing},
year = {2004}
}
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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 verb-noun 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 tasktracking tools could be constructed based on automatic classification into this taxonomy. 1
Keyphrases
speech act categorization problem useful tasktracking tool high precision certain category predefined ontology typical email speech act present experimental result deliver information text-classification learning method reasonable recall automatic classification verb-noun pair topical text classification
