research-article

Finding and linking incidents in news

Authors:

Ao Feng,

James AllanAuthors Info & Claims

CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management

Pages 821 - 830

https://doi.org/10.1145/1321440.1321554

Published: 06 November 2007 Publication History

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Abstract

News reports are being produced and disseminated in overwhelming volume, making it difficult to keep up with the newest information. Most previous research in automatic news organization treated news topics as a flat list, ignoring the intrinsic connection among individual reports. We argue that more contextual information within and across the topics will benefit users in their news understanding process.

A news organization infrastructure, incident threading, is proposed in this article. All text snippets describing the occurrence of a real-world happening are combined into a news incident, and a network is composed of incidents that are interconnected by links in certain types. A limited vocabulary of connection types is defined and corresponding rules are established based upon the human experience of news understanding.

The incident threading system is implemented with two different algorithms. One starts from clustering of text passages and then creates links with pre-built rules. The other method defines a global score function over the whole collection and solves the optimization problem with simulated annealing. The former achieves higher accuracy in the identification of incidents and the latter generates better links, which is preferred since the links are more important for the formation of the incident network.

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Index Terms

Finding and linking incidents in news
1. Information systems

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Information & Contributors

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Published In

CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management

November 2007

1048 pages

ISBN:9781595938039

DOI:10.1145/1321440

Co-chair:
Alberto H. F. Laender,
Conference Chairs:
André O. Falcão
Universidade de Lisboa, Portugal
,
Øystein Haug Olsen,
General Chair:
Mário J. Silva
(Universidade de Lisboa, Portugal)
,
Program Chairs:
Ricardo Baeza-Yates,
Deborah L. McGuinness,
Bjorn Olstad

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2007

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November 6 - 10, 2007

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Chang YChen TLin THsieh Y(2022)Using Multi-task Deep Neural Network to Explore Person Interaction from Social Media2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00061(371-376)Online publication date: Nov-2022
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