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
2000
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.
.
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
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
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Date deposited: 27 Feb 2007
Last modified: 16 Mar 2024 02:53
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Contributors
Author:
S. Kim
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