nonlinear regression and statistical properties to evaluate Interactive Question system ( IQA )
Evaluation plays an important role in the interactive question answering (IQA) systems. In the context of evaluating IQA systems, there is partially no specific methodology for evaluating these systems in general. The main problem with designing an assessment method for IQA systems lies, in fact, that is rarely possible to predict interaction part. To this end, human needs to be involved in the evaluation process. In this paper, an appropriate model is presented by introducing a set of built in features for evaluating IQA systems. To conduct the evaluation process, four IQA systems were considered and based on the conversation was exchanged between users and systems, the number 540 samples were considered as suitable data to create a test and training set. After performing the preprocessing on the conversation, the statistical characteristics of the conversation extracted and base on that characteristics matrix was formed. Finally, using linear and nonlinear regression, the human thinking was predicted that the nonlinear power regression with 0.15 MSE was the best model.
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