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- W2520108117 abstract "With the increment in the volume of information, it’s almost impossible for people to assimilate all the news in time. A method to automatically detect hot topics from web news is strongly desired. Existing solutions take different perspectives ranging from identifying frequencies of terms to terms’ distribution or part-of-speech characteristics. However, most of them are either too simplistic or unfitting to the properties of hot topics. Therefore, this paper presents a hot topic detection approach based on bursty term identification. We propose a new bursty term identification approach which considers both frequency and topicality properties to detect the bursty terms and hot topics. A series of experiments have demonstrated that our proposed approach has good performance compared with baseline methods." @default.
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- W2520108117 date "2016-01-01" @default.
- W2520108117 modified "2023-09-24" @default.
- W2520108117 title "Online Hot Topic Detection from Web News Based on Bursty Term Identification" @default.
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- W2520108117 doi "https://doi.org/10.1007/978-3-319-45817-5_32" @default.
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