Article

Strategies for minimising errors in hierarchical web categorisation

Authors:

Hugh E. WilliamsAuthors Info & Claims

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

Pages 525 - 531

https://doi.org/10.1145/584792.584878

Published: 04 November 2002 Publication History

Abstract

On the Web, browsing and searching categories is a popular method of finding documents. Two well-known category-based search systems are the Yahoo!~and DMOZ hierarchies, which are maintained by experts who assign documents to categories. However, manual categorisation by experts is costly, subjective, and not scalable with the increasing volumes of data that must be processed. Several methods have been investigated for effective automatic text categorisation. These include selection of categorisation methods, selection of pre-categorised training samples, use of hierachies, and selection of document fragments or features. In this paper, we further investigate categorisation into Web hierarchies and the role of hierarchical information in improving categorisation effectiveness. We introduce new strategies to reduce errors in hierarchical categorisation. In particular, we propose novel techniques that shift the assignment into higher level categories when lower level assignment is uncertain. Our results show that absolute error rates can be reduced by over 2%.

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Cited By

Armano G(2015)Modelling Progressive FilteringFundamenta Informaticae10.5555/2756707.2756708138:3(285-320)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.5555/2756707.2756708
Qi XDavison B(2009)Web page classificationACM Computing Surveys10.1145/1459352.145935741:2(1-31)Online publication date: 23-Feb-2009
https://dl.acm.org/doi/10.1145/1459352.1459357
Tang LLiu HZhang JAgarwal NSalerno J(2008)Topic taxonomy adaptation for group profilingACM Transactions on Knowledge Discovery from Data10.1145/1324172.13241731:4(1-28)Online publication date: 1-Feb-2008
https://dl.acm.org/doi/10.1145/1324172.1324173
Show More Cited By

Index Terms

Strategies for minimising errors in hierarchical web categorisation
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

November 2002

704 pages

ISBN:1581134924

DOI:10.1145/584792

General Chair:
Charles Nicholas
University of Maryland Baltimore County
,
Program Chairs:
David Grossman
Illinois Institute of Technology
,
Konstantinos Kalpakis
University of Maryland Baltimore County
,
Sajda Qureshi
Erasmus University, Rotterdam
,
Han van Dissel
Erasmus University, Rotterdam
,
Len Seligman
The MITRE Corporation

Copyright © 2002 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 November 2002

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Citations

Cited By

Armano G(2015)Modelling Progressive FilteringFundamenta Informaticae10.5555/2756707.2756708138:3(285-320)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.5555/2756707.2756708
Qi XDavison B(2009)Web page classificationACM Computing Surveys10.1145/1459352.145935741:2(1-31)Online publication date: 23-Feb-2009
https://dl.acm.org/doi/10.1145/1459352.1459357
Tang LLiu HZhang JAgarwal NSalerno J(2008)Topic taxonomy adaptation for group profilingACM Transactions on Knowledge Discovery from Data10.1145/1324172.13241731:4(1-28)Online publication date: 1-Feb-2008
https://dl.acm.org/doi/10.1145/1324172.1324173
Qi XDavison BYu PTsotras VFox ELiu B(2006)Knowing a web page by the company it keepsProceedings of the 15th ACM international conference on Information and knowledge management10.1145/1183614.1183650(228-237)Online publication date: 6-Nov-2006
https://dl.acm.org/doi/10.1145/1183614.1183650
Qiu XSrinivasan PSong MObradovic Z(2006)GO for gene documentsProceedings of the 1st international workshop on Text mining in bioinformatics10.1145/1183535.1183546(43-51)Online publication date: 10-Nov-2006
https://dl.acm.org/doi/10.1145/1183535.1183546
Tang LZhang JLiu HEliassi-Rad TUngar LCraven MGunopulos D(2006)Acclimatizing Taxonomic Semantics for Hierarchical Content ClassificationProceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1150402.1150446(384-393)Online publication date: 20-Aug-2006
https://dl.acm.org/doi/10.1145/1150402.1150446
Shanks VWilliams HKraft DFrieder OHammer JQureshi SSeligman L(2003)Index construction for linear categorisationProceedings of the twelfth international conference on Information and knowledge management10.1145/956863.956926(334-341)Online publication date: 3-Nov-2003
https://dl.acm.org/doi/10.1145/956863.956926

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