A relevance-based topic model for news event tracking

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.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.0 General

General Terms:
Algorithms, Design, Experimentation, Performance, Theory

Keywords:
LDA, event tracking, relevance models, topic models