Automatic Query Expansion Using Data Manifold

Abstract

This paper proposes an automatic query expansion method that combines document re-ranking and standard Rocchio’s relevance feedback. The document re-ranking method ranks the top retrieved documents based on the intrinsic manifold structure collectively revealed by a great amount of data. This is done by using a semi-supervised learning algorithm to integrate pseudo relevant documents with documents to be re-ranked. Given an initial ranked list of retrieved documents, the document re-ranking approach picks a set of documents from the top ones (including query itself) as pseudo relevant documents. In this way, the intrinsic relationship of all the retrieved documents to be re-ranked with the pseudo relevant documents (pseudo irrelevant documents are missing) can be determined via a semi-supervised learning algorithm. Finally, all the retrieved documents can be re-ranked according to above relationship. Evaluation on benchmark corpora show that the approach can achieve much better performance than standard Rocchio’s relevance feedback and performance better than other related approaches.