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. © 2008 Springer Berlin Heidelberg.
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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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5326 LNCS, pp. 201–209). Springer Verlag. https://doi.org/10.1007/978-3-540-88906-9_26
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