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 knowledge rich variant of TAXIND. While both outperform a baseline approach that does not induce taxonomies, there is no significant performance difference between the TAXIND variants. Finally, we discuss how a more comprehensive representation for features should improve measures on TAXIND’s learning and performance tasks.