Towards a SVM-struct based active learning algorithm for least cost semantic annotation

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008
Pages111-114
Publication statusPublished - 2008

Conference

Title2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008
PlaceAustralia
CitySydney, NSW
Period9 - 12 December 2008

Abstract

The recent growing interests in Semantic Web trigger the requirements of annotating various information objects (e.g., documents) on the Web. The main drawback of the existing methods is that they usually require many manually annotated training examples as inputs. This paper proposes a SVM-struct based active learning algorithm for automatic semantic annotation. In particular, the proposed algorithm is underpinned by a novel uncertainty minimization method which can identify the most discriminative examples for re-training so as to reduce the manual annotation cost. Our initial experiments show that the proposed method can achieve comparable annotation performance while requiring a much smaller training set © 2008 IEEE.

Citation Format(s)

Towards a SVM-struct based active learning algorithm for least cost semantic annotation. / Xu, Kaiquan; Liao, Stephen Shaoyi; Lau, Raymond Y. K. et al.
Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008. 2008. p. 111-114 4740739.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review