title: Cluster-based query expansion using external collections in medical information retrieval.
abstract: Utilizing external collections to improve retrieval performance is challenging research because various test collections are created for different purposes. Improving medical information retrieval has also gained much attention as various types of medical documents have become available to researchers ever since they started storing them in machine processable formats. In this paper, we propose an effective method of utilizing external collections based on the pseudo relevance feedback approach. Our method incorporates the structure of external collections in estimating individual components in the final feedback model. Extensive experiments on three medical collections (TREC CDS, CLEF eHealth, and OHSUMED) were performed, and the results were compared with a representative expansion approach utilizing the external collections to show the superiority of our method.
authors.affiliation: Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon, South Korea. Electronic address: ohs@kisti.re.kr.
authors.forename: Heung-Seon
authors.initials: HS
authors.lastname: Oh
authors.affiliation: Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon, South Korea. Electronic address: jyc77@kisti.re.kr.
authors.forename: Yuchul
authors.initials: Y
authors.lastname: Jung
mesh_terms: D016000:Cluster Analysis; D016247:Information Storage and Retrieval; D008962:Models, Theoretical
keywords: External collections; Language models; Query expansion