Term weighting schemes for emerging event detection

Yanghui Rao, Qing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

As an event-based task, Emerging Event Detection (EED) faces the problems of multiple events on the same subject and the evolution of events. Current term weighting schemes for EED exploiting Named Entity, temporal information and Topic Modeling all have their limited utility. In this paper, a new term weighting scheme, which models the sparse aspect, global weight and local weight of each story, is proposed. Then, an unsupervised algorithm based on the new scheme is applied to EED. We evaluate our approach on two datasets from TDT5, and compare it with TFIDF and existing two schemes exploiting Topic Modeling. Experiments on Retrospective and On-line EED show that our scheme yields better results.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
Pages105-112
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 - Macau, China
Duration: 4 Dec 20127 Dec 2012

Publication series

NameProceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012

Conference

Conference2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
Country/TerritoryChina
CityMacau
Period4/12/127/12/12

Keywords

  • emerging event detection
  • latent dirichlet allocation
  • polysemous
  • synonymous

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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