Article

A probabilistic model for retrospective news event detection

Authors:

Zhiwei Li,

Bin Wang,

Mingjing Li,

Wei-Ying MaAuthors Info & Claims

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 106 - 113

https://doi.org/10.1145/1076034.1076055

Published: 15 August 2005 Publication History

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Abstract

Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle.

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Cited By

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Chen XHale SJurgens DSamory MZuckerman EGrabowicz PChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Global News Synchrony and Diversity During the Start of the COVID-19 PandemicProceedings of the ACM Web Conference 202410.1145/3589334.3645645(2639-2650)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645645
Gautam ARaza Z(2024)Disease outbreak prediction using natural language processing: a reviewKnowledge and Information Systems10.1007/s10115-024-02192-6Online publication date: 6-Aug-2024
https://doi.org/10.1007/s10115-024-02192-6
Kim BYang YPark JJang H(2023)Machine Learning Based Representative Spatio-Temporal Event Documents ClassificationApplied Sciences10.3390/app1307423013:7(4230)Online publication date: 27-Mar-2023
https://doi.org/10.3390/app13074230
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Index Terms

A probabilistic model for retrospective news event detection
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval

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Published In

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2005

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SIGIR05

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SIGIR

SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Chen XHale SJurgens DSamory MZuckerman EGrabowicz PChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Global News Synchrony and Diversity During the Start of the COVID-19 PandemicProceedings of the ACM Web Conference 202410.1145/3589334.3645645(2639-2650)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645645
Gautam ARaza Z(2024)Disease outbreak prediction using natural language processing: a reviewKnowledge and Information Systems10.1007/s10115-024-02192-6Online publication date: 6-Aug-2024
https://doi.org/10.1007/s10115-024-02192-6
Kim BYang YPark JJang H(2023)Machine Learning Based Representative Spatio-Temporal Event Documents ClassificationApplied Sciences10.3390/app1307423013:7(4230)Online publication date: 27-Mar-2023
https://doi.org/10.3390/app13074230
Luqman Jamil MPais SCordeiro JDias GPombo N(2023)An Approach for Detecting Dangerous Events from Online Text Using Transformers2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00040(256-262)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00040
Xiao KQian ZQin B(2022)A Survey of Data Representation for Multi-Modality Event Detection and EvolutionApplied Sciences10.3390/app1204220412:4(2204)Online publication date: 20-Feb-2022
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Rani MKaushal S(2022)GeoClustExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118461210:COnline publication date: 30-Dec-2022
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Jamil MPais SCordeiro J(2022)Detection of dangerous events on social media: a critical reviewSocial Network Analysis and Mining10.1007/s13278-022-00980-y12:1Online publication date: 22-Oct-2022
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Jain TSingh BDewang R(2022)Event Detection in Live Twitter Streams Using Tf-Idf and Clustering AlgorithmsData, Engineering and Applications10.1007/978-981-19-4687-5_36(469-480)Online publication date: 12-Oct-2022
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Samanta MMeena YMazumdar A(2022)Efficacy of Online Event Detection with Contextual and Structural BiasesEmerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT10.1007/978-3-031-07012-9_36(419-430)Online publication date: 26-May-2022
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