In this paper we present a novel methodology for textual case-based reasoning. This technique is unique in that it automatically discovers case and similarity knowledge, is language independent, is scaleable and facilitates semantic similarity between cases to be carried out inherently without the need for domain knowledge. In addition it provides an insight into the thematical content of the c...

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...

In this paper, we argue that syntactic analysis is most likely to improve retrieval accuracy in textual case-based reasoning when the task of the system is well-defined and the relationship between queries and cases is specified in terms of this task. We illustrate this claim with an implemented system for syntax-based answer-indexed retrieval, RealDialog.

This is the first paper on textual case-based reasoning to employ collective classification, a methodology for simultaneously classifying related cases that has consistently attained higher accuracies than standard classification approaches when cases are related. Thus far, case-based classifiers have not been examined for their use in collective classification. We introduce Case-Based Collecti...

where we use c |X to denote the projection of c on X. The teaching dimension of C is the smallest number t such that every c ∈ C has a teaching set of size no more than t [GK95]. However, teaching dimension does not always capture the cooperation in teaching and learning, and the notion of recursive teaching dimension has been introduced and studied extensively in the literature [Kuh99, DSZ10, ...

We introduce a new model of teaching named “preference-based teaching” and a corresponding complexity parameter—the preference-based teaching dimension (PBTD)—representing the worst-case number of examples needed to teach any concept in a given concept class. Although the PBTD coincides with the well-known recursive teaching dimension (RTD) on finite classes, it is radically different on infini...

This study was an attempt to investigate the comparative impact of two types of teaching approaches, namely content-based (CBI) and task-based (TBLT) instruction on the reading comprehension of Iranian EFL learners. For this purpose, sixty intermediate students from a pool of eighty five students studying at a private language school were chosen using a piloted PET. The students were then rando...