Session topic mining for interactive text based on conversational content - Jie PENG; Yongge SHI; Shengbao GAO (Dianxin kexue)
Traditional theme mining model generally digs out the document theme from the interactive text only.In order to explore the session topic and improve the universality of mining model,a kind of interactive text session topic generation model based on the content of the dialogue was put forward.Firstly,by analyzing the characteristics of interactive text and based on the concept of topic tree,a dialog spanning tree was defined with a five-layer structure.Based on this and LDA,the model of session topic generation(ST-LDA)was built.At last,Gibbs sampling method was adopted to deduce the ST-LDA and obtaining session topic and its distribution probability.The results show that the ST-LDA model can dig out a session topic effectively from the interactive text.Besides,the results can reduce the complexity of the classification algorithm and can be back to the theme—participants association.It also has a good universality.