A multi-relational term scheme for first story detection
Author(s)
Xie, Haoran
Wang, Philips Fu Lee
Author(s)
Rao, Y.
Li, Q.
Wu, Q.
Wang, T.
Date Issued
2017
Publisher
Elsevier
Journal
Neurocomputing
Volume
254
Start page
42
End page
52
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.
SFU Affiliated Publication
Yes
Availability at SFU Library
No database links found.