Nirmalie Wiratunga

Learn 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)
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)
Latent Semantic Indexing (LSI) has been shown to be effective in recovering from synonymy and polysemy 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 is(More)
Design is a complex open-ended task and it is unreasonable to expect a case-base to contain representatives of all possible designs. Therefore, adaptation is a desirable capability for case-based design systems, but acquiring adaptation knowledge can involve significant effort. In this paper adaptation knowledge is induced separately for different criteria(More)
This paper looks at feature selection for ordinal text classification. Typical applications are sentiment and opinion classification, where classes have relationships based on an ordinal scale. We show that standard feature selection using Information Gain (IG) fails to identify discriminatory features, particularly when they are distributed over multiple(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)
The High Accuracy Retrieval from Documents (HARD) track explores methods of improving the accuracy of document retrieval systems. As part of this track, the participants have investigated how information about a searcher’s context can be used to improve retrieval performance [Allan, 2003; Allan, 2004]. Searchers, referred to as assessors in this track,(More)