Sub-topic detection and tracking based on dependency connection weights for vector space model - Xue-guang ZHOU; Fei GAO; Yan SUN (Tongxin xuebao)
Aiming at the phenomenon that there are abrupt reports,similar topics and abundant levels of subtopics in the news,a novel method based on relationship analysis using dependent sentence pattern was proposed for sub-topic detection and tracking (sTDT),which constructed feature dimensions to generate the global vectors according to the increment of TF-IDF,and then created the partial adjoin map based on the connection weights within the time window and decreased the dimensions through dependent sentence pattern.Finally,a novel method for sTDT computing was built with adjoins dictionary weights and time threshold attenuation.Experiments show that the proposed method transferrs the text from linear to plane structure,and extracts the subtopics effectively,of which the minimum DET cost is reduced by at least 2.2 percent than that of classical methods.