Abstract
This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take advantage of the hierarchy by considering only second-level categories that exceed a threshold at the top level.
Keywords
HierarchyComputer scienceHierarchical database modelHierarchical control systemInformation retrievalHierarchical organizationContent (measure theory)Multilevel modelArtificial intelligenceData miningMachine learningMathematicsControl (management)
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Publication Info
- Year
- 2000
- Type
- article
- Pages
- 256-263
- Citations
- 801
- Access
- Closed
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Cite This
Susan Dumais,
Hao Chen
(2000).
Hierarchical classification of Web content.
, 256-263.
https://doi.org/10.1145/345508.345593
Identifiers
- DOI
- 10.1145/345508.345593