Article

Event threading within news topics

Authors:

Ramesh Nallapati,

Ao Feng,

Fuchun Peng,

James AllanAuthors Info & Claims

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

Pages 446 - 453

https://doi.org/10.1145/1031171.1031258

Published: 13 November 2004 Publication History

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Abstract

With the overwhelming volume of online news available today, there is an increasing need for automatic techniques to analyze and present news to the user in a meaningful and efficient manner. Previous research focused only on organizing news stories by their topics into a flat hierarchy. We believe viewing a news topic as a flat collection of stories is too restrictive and inefficient for a user to understand the topic quickly.

In this work, we attempt to capture the rich structure of events and their dependencies in a news topic through our event models. We call the process of recognizing events and their dependencies <i>event threading</i>. We believe our perspective of modeling the structure of a topic is more effective in capturing its semantics than a flat list of on-topic stories.

We formally define the novel problem, suggest evaluation metrics and present a few techniques for solving the problem. Besides the standard word based features, our approaches take into account novel features such as temporal locality of stories for event recognition and time-ordering for capturing dependencies. Our experiments on a manually labeled data sets show that our models effectively identify the events and capture dependencies among them.

References

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James Allan, editor. Topic Detection and Tracking:Event based Information Organization. Kluwer Academic Publishers, 2000.

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Cited By

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Zou ZJi XLi Y(2024)A Framework Model of Mining Potential Public Opinion Events Pertaining to Suspected Research Integrity Issues with the Text Convolutional Neural Network model and a Mixed Event ExtractorInformation10.3390/info1506030315:6(303)Online publication date: 24-May-2024
https://doi.org/10.3390/info15060303
Han ZShi LLiu LJiang LFang JLin FZhang JPanneerselvam JAntonopoulos N(2024)A Survey on Event Tracking in Social Media Data StreamsBig Data Mining and Analytics10.26599/BDMA.2023.90200217:1(217-243)Online publication date: Mar-2024
https://doi.org/10.26599/BDMA.2023.9020021
Ge JShi Lliu LHan ZMiller A(2024)DIEET: Knowledge–Infused Event Tracking in Social Media based on Deep LearningPeer-to-Peer Networking and Applications10.1007/s12083-024-01677-zOnline publication date: 17-Apr-2024
https://doi.org/10.1007/s12083-024-01677-z
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Index Terms

Event threading within news topics
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

November 2004

678 pages

ISBN:1581138741

DOI:10.1145/1031171

General Chair:
David Grossman
Illinois Institute of Technology
,
Program Chairs:
Luis Gravano
Columbia University
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign
,
Otthein Herzog
University of Bremen, Germany
,
David A. Evans
Clairvoyance Corporation

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

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Published: 13 November 2004

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Zou ZJi XLi Y(2024)A Framework Model of Mining Potential Public Opinion Events Pertaining to Suspected Research Integrity Issues with the Text Convolutional Neural Network model and a Mixed Event ExtractorInformation10.3390/info1506030315:6(303)Online publication date: 24-May-2024
https://doi.org/10.3390/info15060303
Han ZShi LLiu LJiang LFang JLin FZhang JPanneerselvam JAntonopoulos N(2024)A Survey on Event Tracking in Social Media Data StreamsBig Data Mining and Analytics10.26599/BDMA.2023.90200217:1(217-243)Online publication date: Mar-2024
https://doi.org/10.26599/BDMA.2023.9020021
Ge JShi Lliu LHan ZMiller A(2024)DIEET: Knowledge–Infused Event Tracking in Social Media based on Deep LearningPeer-to-Peer Networking and Applications10.1007/s12083-024-01677-zOnline publication date: 17-Apr-2024
https://doi.org/10.1007/s12083-024-01677-z
Yang JHuang SHuang M(2024)Event Evolution Analysis of Network Text Based on Pre-trained Language Model and Event GraphCooperative Design, Visualization, and Engineering10.1007/978-3-031-71315-6_6(52-62)Online publication date: 15-Sep-2024
https://doi.org/10.1007/978-3-031-71315-6_6
李崭(2023)Topic Detection and Tracking Technology Based on MS-Cluster and Prompt-LearningComputer Science and Application10.12677/CSA.2023.131019013:10(1918-1927)Online publication date: 2023
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A new method of event relation identification

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