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Textual CBR systems solve problems by reusing experiences that are in textual form. Knowledge-rich comparison of textual cases remains an important challenge for these systems. However mapping text data into a structured case representation requires a significant knowledge engineering effort. In this paper we look at automated acquisition of the case(More)
Creating case representations in unsupervised textual case-based reasoning applications is a challenging task because class knowledge is not available to aid selection of discriminatory features or to evaluate alternative system design configurations. Representation is considered as part of the development of a tool, called CAM, which supports an anomaly(More)
Feature selection for unsupervised tasks is particularly challenging, especially when dealing with text data. The increase in online documents and email communication creates a need for tools that can operate without the supervision of the user. In this paper we look at novel feature selection techniques that address this need. A distributional similarity(More)
Case-Based Reasoning (CBR) solves problems by reusing past problem-solving experiences maintained in a casebase. The key CBR knowledge container therefore is its casebase. However there are further containers such as similarity, reuse and revision knowledge that are also crucial. Automated acquisition approaches are particularly attractive to discover(More)
Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can be reused directly. But for design tasks it is common for the retrieved solution to be regarded as an initial(More)
Latent Semantic Indexing (LSI) has been shown to be effective in recovering from synonymy and pol-ysemy in text retrieval applications. However, since LSI ignores class labels of training documents, LSI generated representations are not as effective in classification tasks. To address this limitation, a process called 'sprinkling' is presented. Sprinkling(More)