Context Modeling for Ranking and Tagging Bursty Features in Text Streams

• Main Authors: ZHAO, Xin, JIANG, Jing, HE, Jing, LI, Xiaoming, YAN, Hongfei, Shan, Dongdong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: bursty features; bursty features ranking; bursty feature tagging; context modeling; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1314; https://ink.library.smu.edu.sg/context/sis_research/article/2313/viewcontent/p1769_zhao.pdf
• Institution: Singapore Management University

Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting...