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Query expansion is an information retrieval technique in which new query terms are selected to improve search performance. Although useful terms can be extracted from documents whose relevance is already known, it is difficult to get enough of such feedback from a user in actual use. We propose a query expansion method that performs well even if a user makes practically minimum effort, that is, chooses only a single relevant document. To improve searches in these conditions, we made two refinements to a well-known query expansion method. One uses transductive learning to obtain pseudo relevant documents, thereby increasing the total number of source documents from which expansion terms can be extracted. The other is a modified parameter estimation method that aggregates the predictions of multiple learning trials to sort candidate terms for expansion by importance. Experimental results show that our method outperforms traditional methods, and is comparable to a state of the art method.
Information Search and Retrieval, Query formulation, H.3.3.f Relevance feedback, Machine learning

S. Yamada and M. Okabe, "Semisupervised Query Expansion with Minimal Feedback," in IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. , pp. 1585-1589, 2007.
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