Maximizing Answer Relevance in Community Question Answering with Low Computational Overhead: Insights from Clustering and Fuzzy-Ranking
Keywords:
Community Question Answering (CQA), Triangular Fuzzy Numbers (TFN), String Similarity, Clustering, Similarity IndexAbstract
The primary objective of a question answering system is to retrieve high-quality answers, yet numerous new questions often remain unanswered effectively. In response to this challenge, we present a novel approach that enables users to input a question, receiving a collection of closely related questions. This method aims to provide users with satisfactory responses promptly, eliminating the need for extended waits for answers from other users. Our approach employs a string similarity coupled with a clustering in order to retrieve and group questions that are similar or closely related from the dataset. However, addressing the problem isn't merely about ranking similar question-answer pairs. We also take into account expert ratings of answers, treating them as indices or ratings of user satisfaction. To incorporate these ratings into our framework, we utilize Triangular Fuzzy Numbers (TFN). The average of TFN and the similarity index of the question yields a precise measure of answer satisfaction. In our experimental setup, we utilize a health domain dataset containing user-generated health-related questions. The experimental results conducted on health Community Question Answering (CQA) datasets affirm the superior performance of our proposed method. In predicting suitable answer for new questions, our method demonstrates a higher accuracy compared to other approaches. The effectiveness of our approach is particularly notable in the context of health-related inquiries, showcasing its potential to deliver more precise and reliable outcomes.
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