A multi-relational term scheme for first story detection

Y. Rao, Qing Li, Q. Wu, H. Xie, F.L. Wang, T. Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

© 2017 Elsevier B.V.First Story Detection (FSD) aims to identify the first story for an emerging event previously unreported, which is essential to practical applications in news analysis, intelligence gathering, and national security. Compared to information retrieval, text clustering, text classification, and other subject-based tasks, FSD is event-based and thus faces the challenging issues of multiple events on the same subject and the evolution of events. To tackle these challenges, several schemes for exploiting temporal information, named entity, and topic modeling, have been proposed for FSD. In this paper, we present a new term weighting scheme called LGT, which jointly models the Local element, Global element, and Topical association of each story. An unsupervised algorithm based on LGT is then devised and applied to FSD. We evaluate 4 feature reduction strategies and test our LGT scheme on an online model. Experiments show that our approach yields better results than existing baseline schemes on both retrospective and online FSD.
Original languageEnglish
Pages (from-to)42-52
Number of pages11
JournalNeurocomputing
Volume254
DOIs
Publication statusPublished - 6 Sep 2017
Externally publishedYes

Keywords

  • Feature reduction
  • First story detection
  • Latent Dirichlet allocation
  • polysemous
  • Synonymous

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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