QueryCat: automatic categorization of MEDLINE queries.
A searcher's inability to formulate an appropriate query can result in an overwhelming number of retrieved documents. Our approach to this problem is to use information about common types or categories of queries to (1) reformulate the user's initial query and (2) create an informative organization of the retrieved documents from the reformulated query. To achieve these goals, we first must identify which common categories or types of queries are the best abstraction of the user's specific query. In this paper, we describe a system that performs this first step of categorizing the user's query. Our system uses a two-phased approach: a lexical analysis phase, and a semantic analysis phase. An evaluation of our system demonstrates that its query categorization corresponds reasonably well to the query categorizations by medical librarians and physicians.
Pratt
W
W
Information and Computer Science Department, University of California, Irvine, USA. pratt@ics.uci.edu
Wasserman
H
H
D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D016247:Information Storage and Retrieval / Q000145:classification* / Q000379:methods*; D016239:MEDLINE; D008491:Medical Informatics Applications; D012680:Sensitivity and Specificity; D017432:Unified Medical Language System