article

Topic tracking with time granularity reasoning

Authors:

Qin LuAuthors Info & Claims

ACM Transactions on Asian Language Information Processing (TALIP), Volume 5, Issue 4

Pages 388 - 412

https://doi.org/10.1145/1236181.1236186

Published: 01 December 2006 Publication History

Abstract

Temporal information is an important attribute of a topic, and a topic usually exists in a limited period. Therefore, many researchers have explored the utilization of temporal information in topic detection and tracking (TDT). They use either a story's publication time or temporal expressions in text to derive temporal relatedness between two stories or a story and a topic. However, past research neglects the fact that people tend to express a time with different granularities as time lapses. Based on a careful investigation of temporal information in news streams, we propose a new strategy with time granularity reasoning for utilizing temporal information in topic tracking. A set of topic times, which as a whole represent the temporal attribute of a topic, are distinguished from others in the given on-topic stories. The temporal relatedness between a story and a topic is then determined by the highest coreference level between each time in the story and each topic time where the coreference level between a test time and a topic time is inferred from the two times themselves, their granularities, and the time distance between the topic time and the publication time of the story where the test time appears. Furthermore, the similarity value between an incoming story and a topic, that is the likelihood that a story is on-topic, can be adjusted only when the new story is both temporally and semantically related to the target topic. Experiments on two different TDT corpora show that our proposed method could make good use of temporal information in news stories, and it consistently outperforms the baseline centroid algorithm and other algorithms which consider temporal relatedness.

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Cited By

Gaul WVincent D(2017)Evaluation of the evolution of relationships between topics over timeAdvances in Data Analysis and Classification10.1007/s11634-016-0241-211:1(159-178)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s11634-016-0241-2
Li YLiu CZhao MLi RXiao HWang KZhang J(2016)Multi-Topic Tracking Model for dynamic social networkPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2016.02.038454(51-65)Online publication date: Jul-2016
https://doi.org/10.1016/j.physa.2016.02.038
Zhao XJin PYue L(2015)Discovering topic time from web newsInformation Processing and Management: an International Journal10.1016/j.ipm.2015.04.00151:6(869-890)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1016/j.ipm.2015.04.001
Show More Cited By

Index Terms

Topic tracking with time granularity reasoning
1. Applied computing
  1. Document management and text processing
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Transactions on Asian Language Information Processing

ACM Transactions on Asian Language Information Processing Volume 5, Issue 4

December 2006

148 pages

ISSN:1530-0226

EISSN:1558-3430

DOI:10.1145/1236181

Issue’s Table of Contents

Copyright © 2006 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2006

Published in TALIP Volume 5, Issue 4

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Cited By

Gaul WVincent D(2017)Evaluation of the evolution of relationships between topics over timeAdvances in Data Analysis and Classification10.1007/s11634-016-0241-211:1(159-178)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s11634-016-0241-2
Li YLiu CZhao MLi RXiao HWang KZhang J(2016)Multi-Topic Tracking Model for dynamic social networkPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2016.02.038454(51-65)Online publication date: Jul-2016
https://doi.org/10.1016/j.physa.2016.02.038
Zhao XJin PYue L(2015)Discovering topic time from web newsInformation Processing and Management: an International Journal10.1016/j.ipm.2015.04.00151:6(869-890)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1016/j.ipm.2015.04.001
Zhang SLi YWang XXie L(2013)The research on topic detection based on multi-models and multi-characteristics2013 IEEE 4th International Conference on Software Engineering and Service Science10.1109/ICSESS.2013.6615379(595-598)Online publication date: May-2013
https://doi.org/10.1109/ICSESS.2013.6615379
ZHANG XWANG T(2011)TOPIC TRACKING WITH IMPROVED REPRESENTATION MODEL AND JOINT TRACKING METHODInternational Journal of Wavelets, Multiresolution and Information Processing10.1142/S021969131000386908:06(913-930)Online publication date: 21-Nov-2011
https://doi.org/10.1142/S0219691310003869
Wei Zhang Shuping Li Xue Wang Chunyan Xia (2011)Application of data mining in agricultural topic trackingProceedings of 2011 International Conference on Computer Science and Network Technology10.1109/ICCSNT.2011.6181903(38-41)Online publication date: Dec-2011
https://doi.org/10.1109/ICCSNT.2011.6181903
Liu XMa FLin HShen H(2010)Hypergraph Partition with Harmonic Average Top-N and PCA for Topic DetectionProceedings of the 2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming10.1109/PAAP.2010.38(269-276)Online publication date: 18-Dec-2010
https://dl.acm.org/doi/10.1109/PAAP.2010.38
Zheng WZhang YHong YFan JLiu T(2008)Topic tracking based on keywords dependency profileProceedings of the 4th Asia information retrieval conference on Information retrieval technology10.5555/1786374.1786392(129-140)Online publication date: 15-Jan-2008
https://dl.acm.org/doi/10.5555/1786374.1786392
Zheng WZhang YHong YFan JLiu T(2008)Topic Tracking Based on Keywords Dependency ProfileInformation Retrieval Technology10.1007/978-3-540-68636-1_13(129-140)Online publication date: 2008
https://doi.org/10.1007/978-3-540-68636-1_13

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