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Enhancing topic tracking with temporal information

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Published:06 August 2006Publication History

ABSTRACT

In this paper, we propose a new strategy with time granularity reasoning for utilizing temporal information in topic tracking. Compared with previous ones, our work has four distinguished characteristics. Firstly, we try to determine a set of topic times for a target topic from the given on-topic stories. It helps to avoid the negative influence from other irrelevant times. Secondly, we take into account time granularity variance when deciding whether a coreference relationship exists between two times. Thirdly, both publication time and times presented in texts are considered. Finally, as time is only one attribute of a topic, we increase the similarity between a story and a target topic only when they are related not only temporally but also semantically. Experiments on two TDT corpora show that our method makes good use of temporal information in news stories.

References

  1. James Allan et al. 1998. Topic Detection and Tracking Pilot Study: Final Report. In Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop.Google ScholarGoogle Scholar
  2. Pyung Kim et al. 2004. Usefulness of temporal information automatically extracted from news articles for topic tracking. ACM TALIP, 3(4), pages 227--242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Juha Makkonen et al. 2004. Simple Semantics in Topic Detection and Tracking. Information Retrieval, Vol. 7, pages 347--368. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Baoli Li et.al. 2005. Profile-based Event Tracking. In: Proceedings of the SIGIR-2005 Conference, pages 631--632. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Enhancing topic tracking with temporal information

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      • Published in

        cover image ACM Conferences
        SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
        August 2006
        768 pages
        ISBN:1595933697
        DOI:10.1145/1148170

        Copyright © 2006 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 6 August 2006

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        Overall Acceptance Rate792of3,983submissions,20%

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