Topic-based Probabilistic Document Correlation Model
Shi Shi-xu · 2008
Existing models on document relationship analysis have a difficulty in learning document correlation from topic level.To overcome this difficulty,a topic-based probabilistic document correlation model(TPDC)was proposed.The model learns the topic structure of a document through the latent dirichlet allocation model,infers the posterior probability of a document by computing the posterior probability of its topics and topic similarity,and then constructs the document correlation model based on the document posterior probability.Experimental results show that the TPDC model outperforms the vector space model in retrieval precision and document compression.So the TPDC model is more competent for document retrieval tasks in application.