Topic detection model based on Bayesian belief network
WU Shu-fan · Jisuanji yingyong yanjiu · 2014
According to the research of Bayesian belief network was applied to topic detection,this paper proposed a new topic detection model. The topology of the new model included four level nodes: new story,story term,event term and topic,arcs indicated the indexing relationships. To achieve the task of topic detection,the new model applied conditional probability based on Bayesian probability and conditional independence assumption to compute the similarity between new story and topic clusters. Considering the importance of seminal stories and seminal events,it adjusted weight computations in different levels,and evaluated the new model and the vector space model by DET curves. Experimental results show that adjusted weight computations will improve the performance of the new model,and at the same threshold,the new model has lower miss probability and false alarm probability compared to the vector space model.