Sciweavers

EWCBR
2004
Springer

Learning Feature Taxonomies for Case Indexing

14 years 3 months ago
Learning Feature Taxonomies for Case Indexing
Taxonomic case retrieval systems significantly outperform standard conversational case retrieval systems. However, their feature taxonomies, which are the principal reason for their superior performance, must be manually developed. This is a laborious and error prone process. In an earlier paper, we proposed a framework for automatically acquiring features and organizing them into taxonomies to reduce the taxonomy acquisition effort. In this paper, we focus on the second part of this framework: automated feature organization. We introduce TAXIND, an algorithm for inducing taxonomies from a given set of features; it implements a step in our FACIT framework for knowledge extraction. TAXIND builds taxonomies using a novel bottom up procedure that operates on a matrix of asymmetric similarity values. We introduce measures for evaluating taxonomy induction performance and use them to evaluate TAXIND’s learning performance on two case bases. We investigate both a knowledge poor and a knowl...
Kalyan Moy Gupta, David W. Aha, Philip G. Moore
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where EWCBR
Authors Kalyan Moy Gupta, David W. Aha, Philip G. Moore
Comments (0)