Applying incremental tree induction to retrieval from manuals and medical texts

Kieran J. White and Richard F. E. Sutcliffe

Journal of the American Society for Information Science and Technology, 2006, vol. 57, issue 5, 588-600

Abstract: The Decision Tree Forest (DTF) is an architecture for information retrieval that uses a separate decision tree for each document in a collection. Experiments were conducted in which DTFs working with the incremental tree induction (ITI) algorithm of Utgoff, Berkman, and Clouse (1997) were trained and evaluated in the medical and word processing domains using the Cystic Fibrosis and SIFT collections. Performance was compared with that of a conventional inverted index system (IIS) using a BM25‐derived probabilistic matching function. Initial results using DTF were poor compared to those obtained with IIS. We then simulated scenarios in which large quantities of training data were available, by using only those parts of the document collection that were well covered by the data sets. Consequently the retrieval effectiveness of DTF improved substantially. In one particular experiment precision and recall for DTF were 0.65 and 0.67 respectively, values that compared favorably with values of 0.49 and 0.56 for IIS.

Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:57:y:2006:i:5:p:588-600

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