Abstract: Topic model can help pseudo feedback in query expansion. The main shortcoming of classic topic model is that a topic level needs to be assumed. For constructing the topic levels of closing users on corpus and creating the mapping between topics and words， social annotation was introduced into classic topic model LDA(latent dirichlet allocation)， and a threelevel topic model of topic， label and word was constructed， which was applied to choose query expansion of pseudo feedback. The results showed that this model can describe the semantic of the label， and extract the expansion items that covered the query. The model’s NDCG values are higher than those of the classic pseudo feedback and result set clustering.

Key words: topic model, pseudo feedback, query expansion, word extraction, social annotation