A multi-relational term scheme for first story detection

Yanghui RAO, Qing LI, Qingyuan WU, Haoran XIE, Fu Lee WANG, Tao WANG

Research output: Contribution to journalArticles

11 Citations (Scopus)

Abstract

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. Copyright © 2017 Elsevier B.V.
Original languageEnglish
Pages (from-to)42-52
JournalNeurocomputing
Volume254
Early online dateMar 2017
DOIs
Publication statusPublished - Sep 2017

Citation

Rao, Y., Li, Q., Wu, Q., Xie, H., Wang, F. L., & Wang, T. (2017). A multi-relational term scheme for first story detection. Neurocomputing, 254, 42-52.

Keywords

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

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