The University of Southampton
University of Southampton Institutional Repository

A hybrid user model in text categorisation

A hybrid user model in text categorisation
A hybrid user model in text categorisation
A user model that specifies user preferences on message handling is an essential component of an email message categorizer. We present an approach that combines two learning algorithms, i.e. the Naive Bayesian Classifier (NBC) and Progol, to model implicitly and explicitly reflected user preferences that may not be modelled by using either the algorithms alone. An experiment demonstrates the improvement of categorization performance compared to that of using the two algorithms independently.
Text categorization, user modelling, symbolic learning, statistical learning, email communication
103-104
Association for Computing Machinery
Kim, S.
583524b9-1ca8-4de5-b235-82c689e2a08a
Hall, W.
11f7f8db-854c-4481-b1ae-721a51d8790c
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Kim, S.
583524b9-1ca8-4de5-b235-82c689e2a08a
Hall, W.
11f7f8db-854c-4481-b1ae-721a51d8790c
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def

Kim, S., Hall, W. and Keane, A.J. (2000) A hybrid user model in text categorisation. In Proceedings of KDD-2000: Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. pp. 103-104 .

Record type: Conference or Workshop Item (Paper)

Abstract

A user model that specifies user preferences on message handling is an essential component of an email message categorizer. We present an approach that combines two learning algorithms, i.e. the Naive Bayesian Classifier (NBC) and Progol, to model implicitly and explicitly reflected user preferences that may not be modelled by using either the algorithms alone. An experiment demonstrates the improvement of categorization performance compared to that of using the two algorithms independently.

Text
kim_00.pdf - Accepted Manuscript
Download (520kB)

More information

Published date: 2000
Venue - Dates: KDD-2000: Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, USA, 2000-08-20 - 2000-08-23
Keywords: Text categorization, user modelling, symbolic learning, statistical learning, email communication

Identifiers

Local EPrints ID: 21404
URI: http://eprints.soton.ac.uk/id/eprint/21404
PURE UUID: 815685e6-d58e-4ef7-b4e9-2eaea9e31ef8
ORCID for W. Hall: ORCID iD orcid.org/0000-0003-4327-7811
ORCID for A.J. Keane: ORCID iD orcid.org/0000-0001-7993-1569

Catalogue record

Date deposited: 27 Feb 2007
Last modified: 16 Mar 2024 02:53

Export record

Contributors

Author: S. Kim
Author: W. Hall ORCID iD
Author: A.J. Keane ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×