A tag-centric discriminative model for web objects classification

Lina Yao*, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

This paper studies web object classification problem with the novel exploration of social tags. More and more web objects are increasingly annotated with human interpretable labels (i.e., tags), which can be considered as an auxiliary attribute to assist the object classification. Automatically classifying web objects into manageable semantic categories has long been a fundamental pre-process for indexing, browsing, searching, and mining heterogeneous web objects. However, such heterogeneous web objects often suffer from a lack of easy-extractable and uniform descriptive features. In this paper, we propose a discriminative tag-centric model for web object classification by jointly modeling the objects category labels and their corresponding social tags and un-coding the relevance among social tags. Our approach is based on recent techniques for learning large-scale discriminative models. We conduct experiments to validate our approach using real-life data. The results show the feasibility and good performance of our approach.

Original languageEnglish
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Place of PublicationNew York
PublisherACM
Pages2247-2250
Number of pages4
ISBN (Print)9781450311564
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: 29 Oct 20122 Nov 2012

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period29/10/122/11/12

Keywords

  • optimization
  • semantic annotation
  • social tagging
  • web objects classification

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