A relevance-based topic model for news event tracking

Viet Ha-Thuc, Yelena Mejova, Christopher Harris, Padmini Srinivasan

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

14 Citations (Scopus)

Abstract

Event tracking is the task of discovering temporal patterns of popular events from text streams. Existing approaches for event tracking have two limitations: scalability and inability to rule out non-relevant portions in text streams. In this study, we propose a novel approach to tackle these limitations. To demonstrate the approach, we track news events across a collection of weblogs spanning a two-month time period.

Original languageEnglish
Title of host publicationProceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009
Pages764-765
Number of pages2
DOIs
Publication statusPublished - 28 Dec 2009
Event32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009 - Boston, MA, United States
Duration: 19 Jul 200923 Jul 2009

Publication series

NameProceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009

Other

Other32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009
CountryUnited States
CityBoston, MA
Period19/7/0923/7/09

Keywords

  • Event tracking
  • LDA
  • Relevance models
  • Topic models

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Ha-Thuc, V., Mejova, Y., Harris, C., & Srinivasan, P. (2009). A relevance-based topic model for news event tracking. In Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009 (pp. 764-765). (Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009). https://doi.org/10.1145/1571941.1572117