A multi-relational term scheme for first story detection
A multi-relational term scheme for first story detection
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.
Rao, Yanghui
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Li, Qing
5cf4c6ed-c6d9-4d16-8f9c-aa1d25040b8c
Wu, Qingyuan
a603b768-c1d4-4be4-adf6-da92d9cad507
Xie, Haoran
da2caa49-8f7b-4469-90a2-e40e64184e46
Wang, Fu Lee
61663f93-cd48-4e59-b6d6-708435fa9086
Wang, Tao
c728baeb-cc3f-4948-bf1a-8d63ed60ea74
Rao, Yanghui
d60ecd86-702b-4fc4-a300-52bdf4371d0e
Li, Qing
5cf4c6ed-c6d9-4d16-8f9c-aa1d25040b8c
Wu, Qingyuan
a603b768-c1d4-4be4-adf6-da92d9cad507
Xie, Haoran
da2caa49-8f7b-4469-90a2-e40e64184e46
Wang, Fu Lee
61663f93-cd48-4e59-b6d6-708435fa9086
Wang, Tao
c728baeb-cc3f-4948-bf1a-8d63ed60ea74
Rao, Yanghui, Li, Qing, Wu, Qingyuan, Xie, Haoran, Wang, Fu Lee and Wang, Tao
(2017)
A multi-relational term scheme for first story detection.
Neurocomputing.
(doi:10.1016/j.neucom.2016.06.089).
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.
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Accepted/In Press date: 28 June 2016
e-pub ahead of print date: 3 March 2017
Organisations:
Social Sciences
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Local EPrints ID: 408416
URI: http://eprints.soton.ac.uk/id/eprint/408416
ISSN: 0925-2312
PURE UUID: 73b1cd1d-6566-4fc4-a6c5-2b0f92c94585
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Date deposited: 20 May 2017 04:03
Last modified: 16 Mar 2024 05:20
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Contributors
Author:
Yanghui Rao
Author:
Qing Li
Author:
Qingyuan Wu
Author:
Haoran Xie
Author:
Fu Lee Wang
Author:
Tao Wang
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