Classified Query Expansion Algorithm Based on Semantic Relation Tree
Jialin Wu · 2009
Introducing semantic computing technology into the query expansion is an important research direction.In this paper we presented a semantic relation tree model which combines with topic selection and local feedback method,classified expand query from the perspective of semantic.Traditional methods exist for the problems,such as the lack of knowledge in the topic,the introduction of irrelevant words and the filter functions are not proper.We introduced Web text classification into the semantic relation tree model to make subject expansion with improving the word filter function and increasing the threshold limit to control noise.The combination of user interaction with the local feedback method not only reduces the user's work in traditional relevance feedback method but also solves the problem of highly dependent primary retrieval result in local feedback.The experimental results on the SMART platform show that this method can increase the rate of recall and precision.