Evaluation Measures for TCBR Systems

Textual-case based reasoning (TCBR) systems where the problem and solution are in free text form are hard to evaluate. In the absence of class information, domain experts are needed to evaluate solution quality, and provide relevance information. This approach is costly and time consuming. We propose three measures that can be used to compare alternate TCBR system configurations, in the absence of class information. The main idea is to quantify alignment as the degree to which similar problems have similar solutions. Two local measures capture this information by analysing similarity between problem and solution neighbourhoods at different levels of granularity, whilst a global measure achieves the same by analyzing similarity between problem and solution clusters. We determine the suitability of the proposed measures by studying their correlation with classifier accuracy on a health and safety incident reporting task. Strong correlation is observed with all three approaches with local measures being slightly superior over the global one.