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A hybrid user model in text categorisation

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)


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 algortihms, 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.

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


Local EPrints ID: 21404
PURE UUID: 815685e6-d58e-4ef7-b4e9-2eaea9e31ef8
ORCID for W. Hall: ORCID iD

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Date deposited: 27 Feb 2007
Last modified: 17 Jul 2017 16:26

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

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