Vocabulary expansion technique for advertisement classification

Jin Yong Jung, Jung Hyun Lee, Jongwoo Ha, Sang-Geun Lee

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Contextual advertising is an important revenue source for major service providers on the Web. Ads classification is one of main tasks in contextual advertising, and it is used to retrieve semantically relevant ads with respect to the content of web pages. However, it is difficult for traditional text classification methods to achieve satisfactory performance in ads classification due to scarce term features in ads. In this paper, we propose a novel ads classification method that handles the lack of term features for classifying ads with short text. The proposed method utilizes a vocabulary expansion technique using semantic associations among terms learned from large-scale search query logs. The evaluation results show that our methodology achieves 4.0% ~ 9.7% improvements in terms of the hierarchical f-measure over the baseline classifiers without vocabulary expansion.

Original languageEnglish
Pages (from-to)1373-1387
Number of pages15
JournalKSII Transactions on Internet and Information Systems
Issue number5
Publication statusPublished - 2012 May 25


  • Advertisement classification
  • Centroid classifier
  • Query log
  • Semantic association
  • Vocabulary expansion

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications


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