On handling textual errors in latent document modeling

Tao Yang, Dongwon Lee

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

Abstract

As large-scale text data become available on the Web, textual errors in a corpus are often inevitable (e.g., digitizing historic documents). Due to the calculation of frequencies of words, however, such textual errors can significantly impact the accuracy of statistical models such as the popular Latent Dirichlet Allocation (LDA) model. To address such an issue, in this paper, we propose two novel extensions to LDA (i.e., TE-LDA and TDE-LDA): (1) The TE-LDA model incorporates textual errors into term generation process; and (2) The TDE-LDA model extends TE-LDA further by taking into account topic dependency to leverage on semantic connections among consecutive words even if parts are typos. Using both real and synthetic data sets with varying degrees of "errors", our TDE-LDA model outperforms: (1) the traditional LDA model by 16%-39% (real) and 20%-63% (synthetic); and (2) the state-of-the-art N-Grams model by ll%-27% (real) and 16%-54% (synthetic).

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages2089-2098
Number of pages10
DOIs
StatePublished - Dec 11 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period10/27/1311/1/13

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All Science Journal Classification (ASJC) codes

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

Cite this

Yang, T., & Lee, D. (2013). On handling textual errors in latent document modeling. In CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (pp. 2089-2098). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/2505515.2505555