RCFGED: Retrospective Coarse and Fine-Grained Event Detection from Online News
2015 IEEE International Conference on Systems, Man, and Cybernetics(2015)
摘要
Recently, Retrospective Event Detection attracts much attention. Most researches focus on detecting coarse-grained events or frequent events. However, they neglect to discover fine grained events or important rare events which are significant for human decision making. The important rare events are different from the frequent events revealing common patterns, their features are unnoticed and cannot be effectively extracted from the historical data. To tackle this issue, a systematic approach called RCFGED is proposed to simultaneously detect coarse and fine-grained events by incorporating Chance Discovery theory with topic modeling. The approach employs a Chance Discovery algorithm called Idea Graph, which mines the latent term relations for converting the corpus into a term graph. Then, a semantic-relation extraction approach is proposed based on topic modeling to enrich the graph. Lastly, a graph analytical method is employed to detect the events from the graph. An experiment demonstrates the superiority of RCFGED by comparing with benchmarks.
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关键词
Retrospective Event Detection,Coarse and Fine-Grained Events,Topic Modeling,Chance Discovery,IdeaGraph
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