Towards noise-resilient document modeling

Tao Yang, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


We introduce a generative probabilistic document model based on latent Dirichlet allocation (LDA), to deal with textual errors in the document collection. Our model is inspired by the fact that most large-scale text data are machine-generated and thus inevitably contain many types of noise. The new model, termed as TE-LDA, is developed from the traditional LDA by adding a switch variable into the term generation process in order to tackle the issue of noisy text data. Through extensive experiments, the efficacy of our proposed model is validated using both real and synthetic data sets.

Original languageEnglish (US)
Title of host publicationCIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
Number of pages4
StatePublished - 2011
Event20th ACM Conference on Information and Knowledge Management, CIKM'11 - Glasgow, United Kingdom
Duration: Oct 24 2011Oct 28 2011

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Other20th ACM Conference on Information and Knowledge Management, CIKM'11
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)


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