Abstract
Currently news flood spreads throughout the web. The techniques of Event Detection and Tracking makes it feasible to gather and structure text information into events which are constructed online automatically and updated temporally. Users are usually eager to browse the whole event evolution. With the huge quantity of documents, it is almost impossible for users to read all of them. In this paper, we formally define the problem of event evolution phases discovery. We introduce a novel and principled model (called EPD), aiming at temporally outlining the entire news development. A news document is usually not atomic but consists of independent news segments related to the same event. Therefore we first employ a latent ingredients extraction method to extract event snippets. Unlike traditional clustering methods, we propose a novel metrics integrating content feature, temporal feature, distribution feature and bursty feature to measure the correlation between snippets along timeline in a specific event. Combined with bursty feature, we introduce a novel method to compute word weight. We employ HAC to group the news snippets into diversified phases. An optimization problem are utilized to decide the number of phases, which makes EPD applied. With our novel evaluation method, empirical experiments on two real datasets show that EPD is effective and outperforms various related algorithms. Automatic event chronicle generated is introduced as a typical application of EPD.
Supported by NSFC with Grant No. 61073081, National Key Technology R&D Pillar Program in the 11th Five-year Plan of China with Research No. 2009BAH47B05, and ZTE University Partnership Fund.
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.
Similar content being viewed by others
References
Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study: Final report, Tech. Rep. (1998)
Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: SIGIR 1998, pp. 28–36 (1998)
Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: SIGIR 1998, pp. 37–45 (1998)
Brants, T., Chen, F., Farahat, A.: A System for new event detection. In: SIGIR 2003, pp. 330–337 (2003)
Franz, M., Ward, T., McCarley, J.S., Zhu, W.: Unsupervised and supervised clustering for topic tracking. In: SIGIR 2001, pp. 310–317 (2001)
Yang, Y., Zhang, J., Carbonell, J., Jin, C.: Topic-conditioned novelty detection. In: KDD 2002, pp. 688–693 (2002)
Liu, S., Merhav, Y., Yee, W.G., Goharian, N., Frieder, O.: A sentence level probabilistic model for evolutionary theme pattern mining from news corpora. In: SAC 2009, pp. 1742–1747 (2009)
Nallapati, R., Feng, A., Peng, F., Allan, J.: Event threading within news topics. In: CIKM 2004, pp. 446–453 (2004)
Feng, A., Allan, J.: Finding and linking incidents in news. In: CIKM 2007, pp. 821–830 (2007)
Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: SIGKDD 2005, pp. 198–207 (2005)
Yang, C., Shi, X.: Discovering event evolution graphs from newswires. In: WWW 2006, pp. 945–946 (2006)
Yan, R., Li, Y., Zhang, Y., Li, X.M.: Event Recognition from News Webpages through Latent Ingredients Extraction. In: Cheng, P.-J., Kan, M.-Y., Lam, W., Nakov, P. (eds.) AIRS 2010. LNCS, vol. 6458, pp. 490–501. Springer, Heidelberg (2010)
Wei, C.-P., Lee, Y.-H., Chiang, Y.-S., Chen, J.-D., Yang, C.C.C.: Discovering event episodes from news corpora: a temporal-based approach. In: ICEC 2009, pp. 72–80 (2009)
Wan, X., Yang, J.: Multi-document summarization using cluster-based link analysis. In: SIGIR 2008, pp. 299–306 (2008)
Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: SIGKDD 2009, pp. 497–506 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kong, L., Yan, R., Jiang, H., Zhang, Y., Gao, Y., Fu, L. (2011). Mining Event Temporal Boundaries from News Corpora through Evolution Phase Discovery. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 6897. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23535-1_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-23535-1_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23534-4
Online ISBN: 978-3-642-23535-1
eBook Packages: Computer ScienceComputer Science (R0)