Abstract
Topic tracking is an important task of Topic Detection and Tracking (TDT). Its purpose is to detect stories, from a stream of news, related to known topics. Each topic is “known” by its association with several sample stories that discuss it. In this paper, we propose a new method to build the keywords dependency profile (KDP) of each story and track topic basing on similarity between the profiles of topic and story. In this method, keywords of a story are selected by document summarization technology. The KDP is built by keywords co-occurrence frequency in the same sentences of the story. We demonstrate this profile can describe the core events in a story accurately. Experiments on the mandarin resource of TDT4 and TDT5 show topic tracking system basing on KDP improves the performance by 13.25% on training dataset and 7.49% on testing dataset comparing to baseline.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Allan, J.: Topic Detection and Tracking: Event-based Information Organization. Kluwer Academic Publishers, Massachusetts (2002)
The, Topic Detection and Tracking (TDT2004) Task Definition and Evaluation Plan (2004), http://www.nist.gov/speech/tests/tdt/tdt2004/TDT04.Eval.Plan.v1.2.pdf
Allan, J., Papka, R., Lavrenko., V.: On-line New Event Detection and Tracking. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 37–45. ACM Press, Melbourne (1998)
Radev, R., Jing, H., Budzikowska, M.: Centroid-based Summarization of Multiple Documents: Sentence Extraction, Utility-based Evaluation, and User Studies. In: ANLP/NAACL 2000 Workshop on Automatic Summarization, Association for Computational Linguistics, Seattle, pp. 21–29 (2000)
Masand, B., Linoff, G., Waltz, D.: Classifying News Stories Using Memory Based Reasoning. In: Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 59–65. ACM Press, Copenhagen
Carbonell, J., Yang, Y., Lafferty, J., Brown, R.D., Pierce, T., Liu, X.: CMU Report on TDT-2: Segmentation, Detection and Tracking. In: Proceedings of the DARPA Broadcast News Workshop, pp. 117–120. Morgan Kauffman Publishers, San Francisco (1999)
Kupiec, J., Pedersen, J., Chen, F.: A Trainable Document Summarizer. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 68–73. ACM Press, Seattle (1995)
Yang, Y., Zhang, J., Carbonell, J., Jin, C.: Topic-conditioned Novelty Detection. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 668–693. ACM Press, Edmonton (2002)
Li, B., Li, W., Lu, Q.: Topic Tracking with Time Granularity Reasoning. ACM Transactions on Asian Language Information Processing (TALIP) 5, 388–412 (2006)
Li, B., Li, W., Lu, Q., Wu, M.: Profile-based Event Tracking. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 631–632. ACM Press, Salvador (2005)
Larkey, L., Feng, F., Connell, M., Lavrenko, V.: Language-specific Models in Multilingual Topic Tracking. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 402–409. ACM Press, Sheffield (2004)
Makkonen, J.: Investigations on Event Evolution in TDT. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, Association for Computational Linguistics. Edmonton, pp. 43–48 (2003)
Shah, C., Croft, W.B., Jensen, D.: Representing Documents with Named Entities for Story Link Detection (SLD). In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 868–869. ACM Press, Virginia (2006)
CMU TEAM-A in TDT 2004 Topic Tracking, http://www.nist.gov/speech/tests/tdt/tdt2004/papers/CMU_A_tracking_TDT2004.ppt
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, W., Zhang, Y., Hong, Y., Fan, J., Liu, T. (2008). Topic Tracking Based on Keywords Dependency Profile. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-68636-1_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68633-0
Online ISBN: 978-3-540-68636-1
eBook Packages: Computer ScienceComputer Science (R0)