Automatic Query Expansion Using Data Manifold
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.
KeywordsInformation Retrieval Ranking Score Query Expansion Mean Average Precision Initial Retrieval
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