Please use this identifier to cite or link to this item:
Title: 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.
Issue Date: 2017
Publisher: Elsevier
Journal: Neurocomputing 
Volume: 254
Start page: 42
End page: 52
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.
DOI: 10.1016/j.neucom.2016.06.089
CIHE Affiliated Publication: Yes
Appears in Collections:CIS Publication

Files in This Item:
File Description SizeFormat
View Online131 BHTMLView/Open
SFX Query Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.