A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Extracting significant time varying features from text
Proceedings of the eighth international conference on Information and knowledge management
Matrices with Low-Rank-Plus-Shift Structure: Partial SVD and Latent Semantic Indexing
SIAM Journal on Matrix Analysis and Applications
Detecting and Browsing Events in Unstructured text
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A System for new event detection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling
IEEE Transactions on Knowledge and Data Engineering
Detecting, categorizing and clustering entity mentions in Chinese text
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Time-dependent event hierarchy construction
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.