Abstract
To help people obtain the most important information daily in the shortest time, a novel framework is presented for simultaneous key entities extraction and significant events mining from daily web news. The technique is mainly based on modeling entities and news documents as weighted undirected bipartite graph, which consists of three steps. First, key entities are extracted by scoring all candidate entities on a specific day and tracking their trends within a specific time window. Second, a weighted undirected bipartite graph is built based on entities and related news documents, then mutual reinforcement is imposed on the bipartite graph to rank both of them. Third, clustering on news articles generates daily significant events. Experimental study shows effectiveness of this approach.
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References
Li, W., Qian, D., Lu, Q., Yuan, C.: Detecting, categorizing and clustering entity mentions in Chinese text. In: SIGIR 2007, pp. 647–654 (2007)
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 (SIGIR 1998) (1998)
Connell, M., Feng, A., Kumaran, G., Raghavan, H., Shah, C., Allan, J.: UMass at TDT 2004. In: 2004 Topic Detection and Tracking Workshop (TDT 2004), Gaithersburg, Maryland, USA (2004)
Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study: Final report. In: Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop (1998)
Chen, C.C., Chen, Y.T., Sun, Y., Chen, M.C.: Life cycle modeling of news events using aging theory. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 47–59. Springer, Heidelberg (2003)
Kleinberg, J.M.: Bursty and hierarchical structure in streams. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 91–101 (2002)
Brants, T., Chen, F.: A system for new event detection. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2003) (2003)
Smith, D.A.: Detecting and browsing events in unstructured text. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002) (2002)
Swan, R.C., Allan, J.: Extracting significant time varying features from text. In: Proceedings of the 1999 ACM CIKM International Conference on Information and Knowledge Management (CIKM 1999) (1999)
Zha, H., Zhang, Z.: On Matrices with Low-rank-plus-shift Structures: Partial SVD and Latent Semantic Indexing. SIAM Journal of Matrix Analysis and Applications 21, 522–536 (1999)
Chen, K.-Y., Luesukprasert, L., Chou, S.-c.T.: Hot topic extraction based on timeline analysis and multidimensional sentence modeling. IEEE transactions on knowledge and data engineering 19(8) (August 2007)
Fung, G.P.C., Yu, J.X., Yu, P.S., Lu, H.: Parameter free bursty events detection in text streams. In: VLDB, pp. 181–192 (2005)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. In: Proceedings of the 9th ACM-SIAM Symposium on Discrete Algorithms (1998)
Fung, G.P.C., Yu, J.X., Liu, H., Yu, P.S.: Time-dependent event hierarchy construction. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2007)
Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: SIGIR, pp. 28–36 (1998)
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Liu, M., Liu, Y., Xiang, L., Chen, X., Yang, Q. (2008). Extracting Key Entities and Significant Events from Online Daily News. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_26
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DOI: https://doi.org/10.1007/978-3-540-88906-9_26
Publisher Name: Springer, Berlin, Heidelberg
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